VIPeR: Viewpoint Invariant Pedestrian Recognition,
Pedestrian dataset. 2007.
WWW Link.
Dataset, Pedestrians.
Yamasaki, M.[Masafumi],
Moving body detection device of camera,
US_Patent5,627,586, May 6, 1997
WWW Link.
BibRef
9705
Seki, S.[Susumu],
Nagaya, S.[Shigeki],
Oka, R.[Ryuichi],
Image recognition method,
US_Patent5,838,839, Nov 17, 1998
WWW Link. template extraction of people
BibRef
9811
Ahmad, S.[Subutai],
Hunter, K.L.[Kevin L.],
Computer vision system for subject characterization,
US_Patent6,031,934, Feb 29, 2000
WWW Link.
BibRef
0002
Curio, C.,
Edelbrunner, J.,
Kalinke, T.,
Tzomakas, C.,
von Seelen, W.,
Walking pedestrian recognition,
ITS(1), No. 3, September 2000, pp. 155-163.
IEEE Abstract.
0402
BibRef
Wöhler, C.[Christian],
Autonomous in situ training of classification modules in real-time
vision systems and its application to pedestrian recognition,
PRL(23), No. 11, September 2002, pp. 1263-1270.
Elsevier DOI
0206
BibRef
Hasegawa, O.[Osamu],
Kanade, T.[Takeo],
Type classification, color estimation, and specific target detection of
moving targets on public streets,
MVA(16), No. 2, February 2005, pp. 116-121.
Springer DOI
0501
BibRef
Cao, X.B.[Xian-Bin],
Qiao, H.[Hong],
Keane, J.,
A Low-Cost Pedestrian-Detection System With a Single Optical Camera,
ITS(9), No. 1, March 2008, pp. 58-67.
IEEE DOI
0803
BibRef
Xu, Y.[Yanwu],
Cao, X.B.[Xian-Bin],
Qiao, H.[Hong],
An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian
Detection,
SMC-B(41), No. 1, February 2011, pp. 107-117.
IEEE DOI
1102
BibRef
Chen, Y.T.,
Chen, C.S.,
Fast Human Detection Using a Novel Boosted Cascading Structure With
Meta Stages,
IP(17), No. 8, August 2008, pp. 1452-1464.
IEEE DOI
0808
BibRef
Bellotto, N.,
Hu, H.S.[Huo-Sheng],
Multisensor-Based Human Detection and Tracking for Mobile Service
Robots,
SMC-B(39), No. 1, February 2009, pp. 167-181.
IEEE DOI
0902
BibRef
Oliveira, L.[Luciano],
Nunes, U.[Urbano],
Peixoto, P.[Paulo],
On Exploration of Classifier Ensemble Synergism in Pedestrian Detection,
ITS(11), No. 1, March 2010, pp. 16-27.
IEEE DOI
1003
BibRef
Tang, S.P.[Shao-Peng],
Goto, S.[Satoshi],
Histogram of Template for Pedestrian Detection,
IEICE(E93-D), No. 7, July 2010, pp. 1737-1744.
WWW Link.
1008
BibRef
Liu, C.[Chang],
Wang, G.J.[Gui-Jin],
Liu, C.X.[Chun-Xiao],
Lin, X.G.[Xing-Gang],
Partial Derivative Guidance for Weak Classifier Mining in Pedestrian
Detection,
IEICE(E94-D), No. 8, August 2011, pp. 1721-1724.
WWW Link.
1108
BibRef
Lee, P.H.,
Lin, Y.L.,
Chen, S.C.,
Wu, C.H.,
Tsai, C.C.,
Hung, Y.P.,
Viewpoint-Independent Object Detection Based on Two-Dimensional
Contours and Three-Dimensional Sizes,
ITS(12), No. 4, December 2011, pp. 1599-1608.
IEEE DOI
1112
Pedestrians and vehicles.
BibRef
Li, S.,
Lu, H.,
Arbitrary Body Segmentation With a Novel Graph Cuts-Based Algorithm,
SPLetters(18), No. 12, December 2011, pp. 753-756.
IEEE DOI
1112
BibRef
Angermann, M.,
Robertson, P.,
FootSLAM: Pedestrian Simultaneous Localization and Mapping Without
Exteroceptive Sensors: Hitchhiking on Human Perception and Cognition,
PIEEE(100), No. Special Centinnial Issue 2012, pp. 1840-1848.
IEEE DOI
1202
BibRef
Lu, H.,
Fang, G.,
Shao, X.,
Li, X.,
Segmenting Human From Photo Images Based on a Coarse-to-Fine Scheme,
SMC-B(42), No. 3, June 2012, pp. 889-899.
IEEE DOI
1202
BibRef
Gualdi, G.[Giovanni],
Prati, A.[Andrea],
Cucchiara, R.[Rita],
Multistage Particle Windows for Fast and Accurate Object Detection,
PAMI(34), No. 8, August 2012, pp. 1589-1604.
IEEE DOI
1206
BibRef
Earlier:
A multi-stage pedestrian detection using monolithic classifiers,
AVSBS11(267-272).
IEEE DOI
1111
BibRef
Earlier:
Multi-stage Sampling with Boosting Cascades for Pedestrian Detection in
Images and Videos,
ECCV10(VI: 196-209).
Springer DOI
1009
BibRef
And:
Perspective and appearance context for people surveillance in open
areas,
UCVP10(13-18).
IEEE DOI
1006
Lower complexity than sliding window search for detection.
See also Contextual Information and Covariance Descriptors for People Surveillance: An Application for Safety of Construction Workers.
BibRef
Ye, Q.,
Han, Z.,
Jiao, J.,
Liu, J.,
Human Detection in Images via Piecewise Linear Support Vector Machines,
IP(22), No. 2, February 2013, pp. 778-789.
IEEE DOI
1302
BibRef
Fang, X.Y.[Xian-Yong],
Zhang, H.[Hu],
Zhou, J.[Jian],
Fast window fusion using fuzzy equivalence relation,
PRL(34), No. 6, 15 April 2013, pp. 670-677.
Elsevier DOI
1303
Sliding window; Human detection; Window fusion; Fuzzy equivalence
relation
BibRef
Nguyen, T.H.B.[Thi-Hai-Binh],
Kim, H.[Hakil],
Novel and efficient pedestrian detection using bidirectional PCA,
PR(46), No. 8, August 2013, pp. 2220-2227.
Elsevier DOI
1304
Pedestrian detection; Object detection; Bidirectional PCA
BibRef
Serra-Toro, C.[Carlos],
Traver, V.J.[V. Javier],
Montoliu, R.[Raúl],
Spatial Recurrences for Pedestrian Classification,
JMIV(47), No. 1-2, September 2013, pp. 108-123.
Springer DOI
1307
BibRef
And: A1, A2, Only:
Exploring Relevance Vector Machines for Faster Pedestrian
Classification,
IbPRIA13(509-516).
Springer DOI
1307
BibRef
Earlier: A1, A2, Only:
A New Pedestrian Detection Descriptor Based on the Use of Spatial
Recurrences,
CAIP11(II: 97-104).
Springer DOI
1109
BibRef
Serra-Toro, C.[Carlos],
Hernández-Górriz, Á.[Ángel],
Traver, V.J.[V. Javier],
Strategies of Dictionary Usages for Sparse Representations for
Pedestrian Classification,
IbPRIA17(96-103).
Springer DOI
1706
BibRef
Tian, H.[Hong],
Duan, Z.[Zhu],
Abraham, A.[Ajith],
Liu, H.B.[Hong-Bo],
A novel multiplex cascade classifier for pedestrian detection,
PRL(34), No. 14, 2013, pp. 1687-1693.
Elsevier DOI
1308
Pedestrian detection
BibRef
Yu, J.[Jaehoon],
Miyamoto, R.,
Onoye, T.,
A Speed-Up Scheme Based on Multiple-Instance Pruning for Pedestrian
Detection Using a Support Vector Machine,
IP(22), No. 12, 2013, pp. 4752-4761.
IEEE DOI
1312
BibRef
And:
Corrections:
IP(23), No. 1, January 2014, pp. 478-478.
IEEE DOI
1402
image classification
BibRef
Golbabaee, M.[Mohammad],
Alahi, A.[Alexandre],
Vandergheynst, P.[Pierre],
SCOOP: A Real-Time Sparsity Driven People Localization Algorithm,
JMIV(48), No. 1, January 2014, pp. 160-175.
Springer DOI
1402
BibRef
Wang, X.G.[Xiao-Gang],
Wang, M.[Meng],
Li, W.[Wei],
Scene-Specific Pedestrian Detection for Static Video Surveillance,
PAMI(36), No. 2, February 2014, pp. 361-374.
IEEE DOI
1402
BibRef
Earlier: A2, A3, A1:
Transferring a generic pedestrian detector towards specific scenes,
CVPR12(3274-3281).
IEEE DOI
1208
BibRef
Earlier: A2, A1, Only:
Automatic adaptation of a generic pedestrian detector to a specific
traffic scene,
CVPR11(3401-3408).
IEEE DOI
1106
graph theory
BibRef
Wang, J.Q.[Jun-Qiu],
Yagi, Y.S.[Yasu-Shi],
Shadow extraction and application in pedestrian detection,
JIVP(2014), No. 1, 2014, pp. 12.
DOI Link
1403
See also Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking.
BibRef
Zhang, L.Y.[Li-Yan],
Kalashnikov, D.V.[Dmitri V.],
Mehrotra, S.[Sharad],
Vaisenberg, R.[Ronen],
Context-based person identification framework for smart video
surveillance,
MVA(25), No. 7, October 2014, pp. 1711-1725.
Springer DOI
1410
BibRef
Vineet, V.[Vibhav],
Warrell, J.[Jonathan],
Torr, P.H.S.[Philip H.S.],
Filter-Based Mean-Field Inference for Random Fields with Higher-Order
Terms and Product Label-Spaces,
IJCV(110), No. 1, December 2014, pp. 290-307.
Springer DOI
1411
BibRef
Earlier:
ECCV12(V: 31-44).
Springer DOI
1210
BibRef
Vineet, V.[Vibhav],
Warrell, J.[Jonathan],
Sturgess, P.[Paul],
Torr, P.H.S.[Philip H.S.],
Improved Initialization and Gaussian Mixture Pairwise Terms for Dense
Random Fields with Mean-field Inference,
BMVC12(73).
DOI Link
1301
BibRef
Vineet, V.[Vibhav],
Warrell, J.[Jonathan],
Ladicky, L.[Lubor],
Torr, P.H.S.[Philip H.S.],
Human Instance Segmentation from Video using Detector-based Conditional
Random Fields,
BMVC11(xx-yy).
HTML Version.
1110
BibRef
Yang, K.[Kai],
Delp, E.J.,
Du, E.[Eliza],
Categorization-based two-stage pedestrian detection system for
naturalistic driving data,
SIViP(8), No. S1, December 2014, pp. 135-144.
WWW Link.
1411
BibRef
Li, W.H.[Wen-Hui],
Ni, H.Y.[Hong-Yin],
Wang, Y.[Ying],
Fu, B.[Bo],
Liu, P.X.[Pei-Xun],
Wang, S.J.[Shou-Jia],
Detection of partially occluded pedestrians by an enhanced cascade
detector,
IET-ITS(8), No. 7, 2014, pp. 621-630.
DOI Link
1411
computer vision
BibRef
Guo, L.J.[Li-Jun],
Cheng, T.T.[Ting-Ting],
Xiao, B.[Bo],
Zhang, R.[Rong],
Zhao, J.Y.[Jie-Yu],
Video human segmentation based on multiple-cue integration,
SP:IC(30), No. 1, 2015, pp. 166-177.
Elsevier DOI
1412
Video segmentation
BibRef
Aly, S.,
Partially occluded pedestrian classification using histogram of
oriented gradients and local weighted linear kernel support vector
machine,
IET-CV(8), No. 6, 2014, pp. 620-628.
DOI Link
1502
computer vision
BibRef
Hu, H.M.[Hai-Miao],
Zhang, X.W.[Xiao-Wei],
Zhang, W.[Wan],
Li, B.[Bo],
Joint global-local information pedestrian detection algorithm for
outdoor video surveillance,
JVCIR(26), No. 1, 2015, pp. 168-181.
Elsevier DOI
1502
Pedestrian detection
BibRef
Zhang, X.W.[Xiao-Wei],
Li, B.[Bo],
Hu, H.M.[Hai-Miao],
Scale-aware hierarchical loss: A multipath RPN for multi-scale
pedestrian detection,
VCIP17(1-4)
IEEE DOI
1804
convolution, feature extraction, graph theory,
learning (artificial intelligence), minimisation,
Scale-aware Weighting
BibRef
Tsitsoulis, A.[Athanasios],
Bourbakis, N.G.[Nikolaos G.],
A Methodology for Extracting Standing Human Bodies From Single Images,
HMS(45), No. 3, June 2015, pp. 327-338.
IEEE DOI
1506
Estimation
BibRef
Flohr, F.[Fabian],
Dumitru-Guzu, M.,
Kooij, J.F.P.,
Gavrila, D.M.[Dariu M.],
A Probabilistic Framework for Joint Pedestrian Head and Body
Orientation Estimation,
ITS(16), No. 4, August 2015, pp. 1872-1882.
IEEE DOI
1508
BibRef
Earlier: A1, A4, Only:
PedCut: an iterative framework for pedestrian segmentation combining
shape models and multiple data cues,
BMVC13(xx-yy).
DOI Link
1402
Detectors
BibRef
Enzweiler, M.[Markus],
Gavrila, D.M.[Dariu M.],
Integrated pedestrian classification and orientation estimation,
CVPR10(982-989).
IEEE DOI
1006
BibRef
Earlier:
A mixed generative-discriminative framework for pedestrian
classification,
CVPR08(1-8).
IEEE DOI
0806
BibRef
de Paulo Carlos, G.[Gérson],
Pedrini, H.[Helio],
Schwartz, W.R.[William Robson],
Classification schemes based on Partial Least Squares for face
identification,
JVCIR(32), No. 1, 2015, pp. 170-179.
Elsevier DOI
1511
BibRef
Earlier:
Fast and Scalable Enrollment for Face Identification Based on Partial
Least Squares,
FG13(1-8)
IEEE DOI
1309
Face identification.
face recognition
BibRef
Schwartz, W.R.[William Robson],
Davis, L.S.[Larry S.],
Pedrini, H.[Helio],
Local Response Context Applied to Pedestrian Detection,
CIARP11(181-188).
Springer DOI
1111
BibRef
Earlier: A1, A3, A2:
Video Compression and Retrieval of Moving Object Location Applied to
Surveillance,
ICIAR09(906-916).
Springer DOI
0907
Storing moving object information for later surveillance analysis.
BibRef
de Melo, V.H.C.[Victor Hugo Cunha],
Leao, S.[Samir],
Menotti, D.[David],
Schwartz, W.R.[William Robson],
An Optimized Sliding Window Approach to Pedestrian Detection,
ICPR14(4346-4351)
IEEE DOI
1412
Accuracy
BibRef
Cunha de Melo, V.H.[Victor Hugo],
Leao, S.[Samir],
Campos, M.[Mario],
Menotti, D.[David],
Schwartz, W.R.[William Robson],
Fast pedestrian detection based on a partial least squares cascade,
ICIP13(4146-4150)
IEEE DOI
1402
Pedestrian detection
BibRef
Schwartz, W.R.[William R.],
Kembhavi, A.[Aniruddha],
Harwood, D.[David],
Davis, L.S.[Larry S.],
Human Detection Using Partial Least Squares Analysis,
ICCV09(24-31).
IEEE DOI
0909
See also Vehicle Detection Using Partial Least Squares.
BibRef
Schwartz, W.R.[William Robson],
Gopalan, R.[Raghuraman],
Chellappa, R.[Rama],
Davis, L.S.[Larry S.],
Robust Human Detection under Occlusion by Integrating Face and Person
Detectors,
ICB09(970-979).
Springer DOI
0906
See also Robust and Scalable Approach to Face Identification, A.
BibRef
Jordão, A.,
de Souza, J.S.,
Schwartz, W.R.,
A late fusion approach to combine multiple pedestrian detectors,
ICPR16(4250-4255)
IEEE DOI
1705
Computational efficiency, Detectors, Feature extraction, Robots,
Surveillance, Windows
BibRef
Vinay, G.K.[G. Krishna],
Haque, S.M.,
Babu, R.V.[R. Venkatesh],
Ramakrishnan, K.R.,
Sparse Representation-Based Human Detection:
A Scale-Embedded dictionary approach,
SIViP(10), No. 3, March 2016, pp. 585-592.
Springer DOI
1602
BibRef
Liu, Y.F.[Yi-Feng],
Zou, L.[Lian],
Li, J.[Jie],
Yan, J.[Jia],
Shi, W.X.[Wen-Xuan],
Deng, D.X.[De-Xiang],
Segmentation by weighted aggregation and perceptual hash for
pedestrian detection,
JVCIR(36), No. 1, 2016, pp. 80-89.
Elsevier DOI
1603
Pedestrian detection
BibRef
Li, Q.,
Yan, Y.,
Wang, H.,
Discriminative Weighted Sparse Partial Least Squares for Human
Detection,
ITS(17), No. 4, April 2016, pp. 1062-1071.
IEEE DOI
1604
Decision trees
BibRef
Htike, K.K.[Kyaw Kyaw],
Efficient Labelling of Pedestrian Supervisions,
ELCVIA(15), No. 1, 2016, pp. 77-99.
DOI Link
DOI Link
1608
BibRef
Nattoji Rajaram, R.,
Ohn-Bar, E.[Eshed],
Trivedi, M.M.[Mohan M.],
Looking at Pedestrians at Different Scales:
A Multiresolution Approach and Evaluations,
ITS(17), No. 12, December 2016, pp. 3565-3576.
IEEE DOI
1612
Computational modeling
BibRef
Chen, X.,
Hwang, J.N.,
Meng, D.,
Lee, K.H.,
de Queiroz, R.L.,
Yeh, F.M.,
A Quality-of-Content-Based Joint Source and Channel Coding for Human
Detections in a Mobile Surveillance Cloud,
CirSysVideo(27), No. 1, January 2017, pp. 19-31.
IEEE DOI
1701
Cameras
BibRef
Cai, Y.W.[Ya-Wei],
Tan, X.S.[Xiao-Song],
Tan, X.Y.[Xiao-Yang],
Selective Weakly Supervised Human Detection under Arbitrary Poses,
PR(65), No. 1, 2017, pp. 223-237.
Elsevier DOI
1702
Weakly supervised learning
BibRef
Li, X.,
Li, L.,
Flohr, F.,
Wang, J.,
Xiong, H.,
Bernhard, M.,
Pan, S.,
Gavrila, D.M.,
Li, K.,
A Unified Framework for Concurrent Pedestrian and Cyclist Detection,
ITS(18), No. 2, February 2017, pp. 269-281.
IEEE DOI
1702
Benchmark testing
BibRef
Novak, A.,
Armstrong, N.,
Caelli, T.M.[Terry M.],
Blair, I.,
Bayesian Contrast Measures and Clutter Distribution Determinants of
Human Target Detection,
IP(26), No. 3, March 2017, pp. 1115-1126.
IEEE DOI
1703
Bayes methods
BibRef
Pak, J.M.,
Ahn, C.K.,
Shmaliy, Y.S.,
Shi, P.,
Lim, M.T.,
Accurate and Reliable Human Localization Using Composite Particle/FIR
Filtering,
HMS(47), No. 3, June 2017, pp. 332-342.
IEEE DOI
1706
Atmospheric measurements, Finite impulse response filters,
Noise measurement, Particle measurements, Receivers, Robustness,
Composite particle/finite impulse response (FIR) filter (CPFF),
human localization, particle, filter, (PF)
BibRef
Bilal, M.,
Khan, A.,
Karim Khan, M.U.,
Kyung, C.M.,
A Low-Complexity Pedestrian Detection Framework for Smart Video
Surveillance Systems,
CirSysVideo(27), No. 10, October 2017, pp. 2260-2273.
IEEE DOI
1710
object detection,
pedestrians, support vector machines, video surveillance,
histogram of oriented gradients, linear support vector machine,
BibRef
Coniglio, C.[Christophe],
Meurie, C.[Cyril],
Lézoray, O.[Olivier],
Berbineau, M.[Marion],
People silhouette extraction from people detection bounding boxes in
images,
PRL(93), No. 1, 2017, pp. 182-191.
Elsevier DOI
1706
People detection
BibRef
Osayamwen, F.[Festus],
Tapamo, J.R.[Jules-Raymond],
Improved eigenspectrum regularisation for human activity recognition,
IJCVR(8), No. 4, 2018, pp. 435-454.
DOI Link
1808
BibRef
Toudjeu, I.T.[Ignace Tchangou],
Tapamo, J.R.[Jules Raymond],
Slope Pattern Spectra for Human Action Recognition,
ICIAR18(381-389).
Springer DOI
1807
BibRef
Brits, A.M.[Alessio M.],
Tapamo, J.R.[Jules R.],
A Shape and Energy Based Approach to Vertical People Separation in
Video Surveillance,
ISVC09(II: 345-356).
Springer DOI
0911
BibRef
Hattori, H.[Hironori],
Lee, N.[Namhoon],
Boddeti, V.N.[Vishnu Naresh],
Beainy, F.[Fares],
Kitani, K.M.[Kris M.],
Kanade, T.[Takeo],
Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator
for Static Video Surveillance,
IJCV(126), No. 9, September 2018, pp. 1027-1044.
Springer DOI
1809
BibRef
Bartoli, F.[Federico],
Lisanti, G.[Giuseppe],
Karaman, S.[Svebor],
del Bimbo, A.[Alberto],
Scene-dependent proposals for efficient person detection,
PR(87), 2019, pp. 170-178.
Elsevier DOI
1812
Person detection, Scene-dependent proposals,
Gaussian mixture model, Scene modelling
BibRef
Kooij, J.F.P.[Julian F.P.],
Flohr, F.[Fabian],
Pool, E.A.I.[Ewoud A.I.],
Gavrila, D.M.[Dariu M.],
Context-Based Path Prediction for Targets with Switching Dynamics,
IJCV(127), No. 3, March 2019, pp. 239-262.
Springer DOI
1903
Objects have multiple dynamic modes.
BibRef
Yao, L.[Li],
Wang, B.F.[Bo-Fan],
Pedestrian detection framework based on magnetic regional regression,
IET-IPR(13), No. 9, 18 July 2019, pp. 1431-1436.
DOI Link
1907
BibRef
Galiyawala, H.[Hiren],
Raval, M.S.[Mehul S.],
Dave, S.[Shivansh],
Visual appearance based person retrieval in unconstrained environment
videos,
IVC(92), 2019, pp. 103816.
Elsevier DOI
1912
Linear filtering, Person retrieval, Semantic description,
Soft biometrics, Video surveillance
BibRef
Zhang, S.,
Xie, Y.,
Wan, J.,
Xia, H.,
Li, S.Z.,
Guo, G.,
WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the
Wild,
MultMed(22), No. 2, February 2020, pp. 380-393.
IEEE DOI
2001
Benchmark testing, Detectors, Training, Urban areas, Cameras,
Task analysis, Deep learning, Pedestrian detection, dataset,
high density
BibRef
Chandrasekar, K.S.[Karnam Silpaja],
Geetha, P.[Planisamy],
Highly efficient neoteric histogram-entropy-based rapid and automatic
thresholding method for moving vehicles and pedestrians detection,
IET-IPR(14), No. 2, February 2020, pp. 354-365.
DOI Link
2001
BibRef
Huang, E.[Enbo],
Su, Z.[Zhuo],
Zhou, F.[Fan],
Wang, R.M.[Ruo-Mei],
Learning rebalanced human parsing model from imbalanced datasets,
IVC(99), 2020, pp. 103928.
Elsevier DOI
2006
Human parsing, Semantic segmentation, Imbalanced datasets
BibRef
Zhao, J.[Jian],
Li, J.S.[Jian-Shu],
Liu, H.Z.[Heng-Zhu],
Yan, S.C.[Shui-Cheng],
Feng, J.S.[Jia-Shi],
Fine-Grained Multi-human Parsing,
IJCV(128), No. 8-9, September 2020, pp. 2185-2203.
Springer DOI
2008
BibRef
Liu, S.[Si],
Ren, G.H.[Guang-Hui],
Sun, Y.[Yao],
Wang, J.Q.[Jin-Qiao],
Wang, C.H.[Chang-Hu],
Li, B.[Bo],
Yan, S.C.[Shui-Cheng],
Fine-Grained Human-Centric Tracklet Segmentation with Single Frame
Supervision,
PAMI(44), No. 2, February 2022, pp. 610-621.
IEEE DOI
2201
Labeling, Object segmentation, Image segmentation, Task analysis,
Semantics, Training, Face, Video object segmentation, human-centric,
optical flow estimation
BibRef
Chiang, S.H.[Sheng-Ho],
Wang, T.[Tsaipei],
Chen, Y.F.[Yi-Fu],
Efficient pedestrian detection in top-view fisheye images using
compositions of perspective view patches,
IVC(105), 2021, pp. 104069.
Elsevier DOI
2101
Pedestrian detection, Fisheye cameras, Omnidirectional cameras
BibRef
Sena, J.[Jessica],
Jordão, A.[Artur],
Schwartz, W.R.[William Robson],
A content-based late fusion approach applied to pedestrian detection,
JVCIR(77), 2021, pp. 103091.
Elsevier DOI
2106
Edestrian detection, Content-based fusion, Spatial consensus,
Multiple detectors, Late fusion
BibRef
Tian, W.[Wei],
Deng, Z.W.[Zhen-Wen],
Yin, D.[Dong],
Zheng, Z.[Zehan],
Huang, Y.[Yuyao],
Bi, X.[Xin],
3D Pedestrian Detection in Farmland by Monocular RGB Image and
Far-Infrared Sensing,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Zhang, X.Z.[Xiang-Zhou],
Ma, B.P.[Bing-Peng],
Chang, H.[Hong],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
Location Sensitive Network for Human Instance Segmentation,
IP(30), 2021, pp. 7649-7662.
IEEE DOI
2109
Image segmentation, Prototypes, Heating systems, Task analysis,
Semantics, Feature extraction, Detectors,
points representation
BibRef
Tan, F.[Fang],
Xia, Z.Q.[Zhao-Qiang],
Ma, Y.P.[Yu-Peng],
Feng, X.Y.[Xiao-Yi],
3D Sensor Based Pedestrian Detection by Integrating Improved HHA
Encoding and Two-Branch Feature Fusion,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Shen, G.[Gelin],
Yu, Y.[Yang],
Tang, Z.R.[Zhi-Ri],
Chen, H.Q.[Hao-Qiang],
Zhou, Z.T.[Zong-Tan],
HQA-Trans: An end-to-end high-quality-awareness image translation
framework for unsupervised cross-domain pedestrian detection,
IET-CV(16), No. 3, 2022, pp. 218-229.
DOI Link
2204
BibRef
Kumar, K.[Kaushal],
Mishra, R.K.[Ritesh Kumar],
A Diagonally Oriented Novel Feature Extractor for Pedestrian
Detection and Its Efficient Hardware Implementation,
CirSysVideo(32), No. 4, April 2022, pp. 2035-2042.
IEEE DOI
2204
Feature extraction, Hardware, Histograms,
Field programmable gate arrays, Mathematical model, pedestrian detection
BibRef
Zhang, T.L.[Tian-Liang],
Ye, Q.X.[Qi-Xiang],
Zhang, B.C.[Bao-Chang],
Liu, J.Z.[Jian-Zhuang],
Zhang, X.P.[Xiao-Peng],
Tian, Q.[Qi],
Feature Calibration Network for Occluded Pedestrian Detection,
ITS(23), No. 5, May 2022, pp. 4151-4163.
IEEE DOI
2205
Feature extraction, Calibration, Detectors, Deep learning,
Visualization, Training, Object detection, Pedestrian detection,
self-paced learning
BibRef
Zhang, S.[Sanyi],
Cao, X.C.[Xiao-Chun],
Qi, G.J.[Guo-Jun],
Song, Z.J.[Zhan-Jie],
Zhou, J.[Jie],
AIParsing: Anchor-Free Instance-Level Human Parsing,
IP(31), 2022, pp. 5599-5612.
IEEE DOI
2209
Task analysis, Detectors, Image edge detection, Semantics, Head,
Proposals, Object detection, Instance-level human parsing,
video human parsing
BibRef
Zhou, C.J.[Cheng-Ju],
Wu, M.Q.[Mei-Qing],
Lam, S.K.[Siew-Kei],
Enhanced Multi-Task Learning Architecture for Detecting Pedestrian at
Far Distance,
ITS(23), No. 9, September 2022, pp. 15588-15604.
IEEE DOI
2209
Feature extraction, Semantics, Proposals, Computational complexity,
Multitasking, Head, Annotations, Multi-task learning,
cascade detection
BibRef
Luo, Y.[Yan],
Zhang, C.Y.[Chong-Yang],
Lin, W.Y.[Wei-Yao],
Yang, X.K.[Xiao-Kang],
Sun, J.[Jun],
Sequential Attention-Based Distinct Part Modeling for Balanced
Pedestrian Detection,
ITS(23), No. 9, September 2022, pp. 15644-15654.
IEEE DOI
2209
Feature extraction, Detectors, Proposals, Task analysis, Sun,
Intelligent transportation systems, Annotations,
part attention
BibRef
Dasgupta, K.[Kinjal],
Das, A.[Arindam],
Das, S.[Sudip],
Bhattacharya, U.[Ujjwal],
Yogamani, S.[Senthil],
Spatio-Contextual Deep Network-Based Multimodal Pedestrian Detection
for Autonomous Driving,
ITS(23), No. 9, September 2022, pp. 15940-15950.
IEEE DOI
2209
Feature extraction, Sensors, Convolution, Cameras, Lighting, Decoding,
Autonomous vehicles, Pedestrian detection, deep learning
BibRef
Xu, H.[He],
Guo, M.[Mingtao],
Nedjah, N.[Nadia],
Zhang, J.[Jindan],
Li, P.[Peng],
Vehicle and Pedestrian Detection Algorithm Based on Lightweight
YOLOv3-Promote and Semi-Precision Acceleration,
ITS(23), No. 10, October 2022, pp. 19760-19771.
IEEE DOI
2210
Convolution, Kernel, Computational modeling, Real-time systems,
Object detection, Feature extraction, Telecommunications,
semi-precision acceleration
BibRef
Yang, J.H.[Jian-Hua],
Wang, K.[Ke],
Li, R.F.[Rui-Feng],
Qin, Z.H.[Zhong-Hao],
Perner, P.[Petra],
A novel fast combine-and-conquer object detector based on only
one-level feature map,
CVIU(224), 2022, pp. 103561.
Elsevier DOI
2211
Deep learning, Object detection, Face detection,
Person detection, Combine-and-conquer
BibRef
Wang, Z.[Zhe],
Wang, J.[Jun],
Yang, Y.Z.[Ye-Zhou],
Xing, J.L.[Jun-Liang],
A Coulomb Force Inspired Loss Function for High-Performance
Pedestrian Detection,
SPLetters(29), 2022, pp. 2318-2322.
IEEE DOI
2212
Force, Proposals, Physics, Detectors, Training, Linear programming,
Energy consumption, Bounding box regression, Coulomb force,
pedestrian detection
BibRef
Akshatha, K.R.,
Karunakar, A.K.,
Shenoy, B.S.[B. Satish],
Pavan, K.P.[K. Phani],
Dhareshwar, C.V.[Chinmay V.],
Johnson, D.G.[Dennis George],
Manipal-UAV person detection dataset: A step towards benchmarking
dataset and algorithms for small object detection,
PandRS(195), 2023, pp. 77-89.
Elsevier DOI
2301
Dataset, UAV Human Detection. Small object detection, Unmanned aerial vehicles,
Convolutional neural networks, Deep learning, Computer vision
BibRef
Xue, P.[Pan],
Chen, H.[Houjin],
Li, Y.F.[Yan-Feng],
Li, J.[Jupeng],
Multi-scale pedestrian detection with global-local attention and
multi-scale receptive field context,
IET-CV(17), No. 1, 2023, pp. 13-25.
DOI Link
2303
BibRef
Qiu, C.R.[Cheng-Run],
Zhang, D.H.[Dong-Heng],
Hu, Y.[Yang],
Li, H.Q.[Hou-Qiang],
Sun, Q.[Qibin],
Chen, Y.[Yan],
Radio-Assisted Human Detection,
MultMed(25), 2023, pp. 2613-2623.
IEEE DOI
2307
Location awareness, Detectors, Proposals, Feature extraction,
Antenna arrays, Wireless communication, Task analysis,
two-stage detector
BibRef
Li, Q.[Qing],
Zhang, C.Q.[Chang-Qing],
Hu, Q.H.[Qing-Hua],
Fu, H.Z.[Hua-Zhu],
Zhu, P.F.[Peng-Fei],
Confidence-Aware Fusion Using Dempster-Shafer Theory for
Multispectral Pedestrian Detection,
MultMed(25), 2023, pp. 3420-3431.
IEEE DOI
2309
BibRef
Zhang, Y.[Yan],
Xu, C.[Chang],
Yang, W.[Wen],
He, G.J.[Guang-Jun],
Yu, H.[Huai],
Yu, L.[Lei],
Xia, G.S.[Gui-Song],
Drone-based RGBT tiny person detection,
PandRS(204), 2023, pp. 61-76.
Elsevier DOI
2310
Aerial images, Thermal images, Tiny person detection, Multi-modal fusion
BibRef
Cai, Y.C.[Yan-Cheng],
Zhang, B.[Bo],
Li, B.[Baopu],
Chen, T.[Tao],
Yan, H.L.[Hong-Liang],
Zhang, J.D.[Jing-Dong],
Xu, J.H.[Jia-Hao],
Rethinking Cross-Domain Pedestrian Detection: A Background-Focused
Distribution Alignment Framework for Instance-Free One-Stage
Detectors,
IP(32), 2023, pp. 4935-4950.
IEEE DOI
2310
BibRef
Zhang, J.H.[Jian-Hua],
Wang, R.[Rucen],
Liu, R.[Ruyu],
Guo, D.Y.[Dong-Yan],
Li, B.[Bo],
Chen, S.Y.[Sheng-Yong],
DSP-Based Traffic Target Detection for Intelligent Transportation,
ITS(24), No. 11, November 2023, pp. 13180-13191.
IEEE DOI
2311
Efficient pedestrian and vehicle detection.
BibRef
Li, R.M.[Rui-Min],
Xiang, J.J.[Jia-Jun],
Sun, F.X.[Fei-Xiang],
Yuan, Y.[Ye],
Yuan, L.W.[Long-Wu],
Gou, S.P.[Shui-Ping],
Multiscale Cross-Modal Homogeneity Enhancement and Confidence-Aware
Fusion for Multispectral Pedestrian Detection,
MultMed(26), 2024, pp. 852-863.
IEEE DOI
2402
Feature extraction, Lighting, Proposals, Task analysis,
Convolutional neural networks, Training, Sun,
multispectral pedestrian detection
BibRef
Li, Q.[Qing],
Zhang, C.Q.[Chang-Qing],
Hu, Q.H.[Qing-Hua],
Zhu, P.F.[Peng-Fei],
Fu, H.Z.[Hua-Zhu],
Chen, L.[Lei],
Stabilizing Multispectral Pedestrian Detection With Evidential Hybrid
Fusion,
CirSysVideo(34), No. 4, April 2024, pp. 3017-3029.
IEEE DOI
2404
Uncertainty, Pedestrians, Reliability, Image color analysis, multispectral,
Feature extraction, Task analysis, Estimation, Multimodal learning
BibRef
Zhang, S.[Shun],
Li, Y.P.[Yu-Peng],
Wu, X.[Xiao],
Chu, Z.H.[Zun-Heng],
Li, L.F.[Ling-Fei],
MRG-T: Mask-Relation-Guided Transformer for Remote Vision-Based
Pedestrian Attribute Recognition in Aerial Imagery,
RS(16), No. 7, 2024, pp. 1216.
DOI Link
2404
BibRef
Chu, F.C.[Fu-Chen],
Cao, J.[Jiale],
Song, Z.J.[Zhan-Jie],
Shao, Z.[Zhuang],
Pang, Y.W.[Yan-Wei],
Li, X.L.[Xue-Long],
Toward Generalizable Multispectral Pedestrian Detection,
ITS(25), No. 5, May 2024, pp. 3739-3750.
IEEE DOI
2405
Pedestrians, Transformers, Proposals, Feature extraction,
Task analysis, Lighting, Detectors, transformer
BibRef
Yu, Y.J.[You-Jiang],
Zhang, K.B.[Kai-Bing],
Wang, X.H.[Xiao-Hua],
Wang, N.N.[Nan-Nan],
Gao, X.B.[Xin-Bo],
An Adaptive Region Proposal Network With Progressive Attention
Propagation for Tiny Person Detection From UAV Images,
CirSysVideo(34), No. 6, June 2024, pp. 4392-4406.
IEEE DOI Code:
WWW Link.
2406
Proposals, Feature extraction, Object detection, Detectors,
Autonomous aerial vehicles, Visualization, Task analysis,
attention mechanism
BibRef
Liu, S.[Sanzai],
Cao, L.H.[Li-Hua],
Li, Y.[Yi],
Lightweight Pedestrian Detection Network for UAV Remote Sensing
Images Based on Strideless Pooling,
RS(16), No. 13, 2024, pp. 2331.
DOI Link
2407
BibRef
Xing, Z.X.[Zhi-Xuan],
Chen, P.H.[Peng-Hui],
Wang, J.[Jun],
Bai, Y.J.[Yu-Jing],
Song, J.[Jinhao],
Tian, L.[Liuyang],
Millimeter-Wave Radar Detection and Localization of a Human in Indoor
Complex Environments,
RS(16), No. 14, 2024, pp. 2572.
DOI Link
2408
BibRef
Li, X.Y.[Xiang-Yang],
Chen, S.[Shiguo],
Tian, C.[Chunna],
Zhou, H.[Heng],
Zhang, Z.X.[Zhen-Xi],
M2FNet: Mask-Guided Multi-Level Fusion for RGB-T Pedestrian Detection,
MultMed(26), 2024, pp. 8678-8690.
IEEE DOI
2408
Pedestrians, Feature extraction, Task analysis, Detectors, Lighting,
Fuses, Uncertainty, Mask-guided, RGB-T fusion
BibRef
Nie, L.Z.[Lin-Zhen],
Lu, M.[Meihe],
He, Z.W.[Zhi-Wei],
Hu, J.C.[Jia-Chen],
Yin, Z.S.[Zhi-Shuai],
Multispectral pedestrian detection based on feature complementation
and enhancement,
IET-ITS(18), No. 11, 2024, pp. 2166-2177.
DOI Link
2411
automated driving, intelligent vehicles, image fusion, infrared imaging
BibRef
Krebs, S.[Sebastian],
Braun, M.[Markus],
Gavrila, D.M.[Dariu M.],
EuroCity Persons 2.0: A Large and Diverse Dataset of Persons in
Traffic,
PAMI(46), No. 12, December 2024, pp. 10929-10943.
IEEE DOI
2411
Annotations, Manuals, Urban areas, Laser radar, Trajectory,
Inference algorithms, Costs, Robot sensing systems, Radar tracking, benchmarking
BibRef
Chen, Z.F.[Ze-Fei],
Lin, Y.J.[Yong-Jie],
Xu, J.M.[Jian-Min],
Lu, K.[Kai],
Huang, Z.[Zihao],
A fused score computation approach to reflect the overlap between the
predicted box and the ground truth in pedestrian detection,
IET-IPR(18), No. 13, 2024, pp. 4287-4296.
DOI Link Code:
WWW Link.
2411
aggregation, data analysis, data communication, data mining
BibRef
Xie, Q.[Qian],
Cheng, T.Y.[Ta-Ying],
Zhong, J.X.[Jia-Xing],
Zhou, K.[Kaichen],
Markham, A.[Andrew],
Trigoni, N.[Niki],
Beyond Fusion: Modality Hallucination-based Multispectral Fusion for
Pedestrian Detection,
WACV24(644-653)
IEEE DOI
2404
Pedestrians, Fuses, Noise, Computer architecture, Feature extraction,
Boosting, Algorithms, Image recognition and understanding
BibRef
Chen, H.[Huan],
Guo, X.M.[Xiao-Ming],
Multi-Scale Feature Fusion Pedestrian Detection Algorithm Based on
Transformer,
CVIDL23(536-540)
IEEE DOI
2403
Visualization, Pedestrians, Lighting, Focusing, Interference,
Feature extraction, Transformers, Pedestrian detection, Attention mechanism
BibRef
Sahin, Y.H.[Yusuf H.],
Abdinli, E.[Elvin],
Aydin, M.A.[M. Arda],
Unal, G.[Gozde],
TinyPedSeg: A Tiny Pedestrian Segmentation Benchmark for Top-Down
Drone Images,
MVA23(1-5)
DOI Link Code:
WWW Link.
2403
Image segmentation, Pedestrians, Surveillance, Neural networks,
Transportation, Object segmentation, Object detection
BibRef
Yang, L.[Lu],
Li, L.[Liulei],
Xin, X.[Xueshi],
Sun, Y.F.[Yi-Fan],
Song, Q.[Qing],
Wang, W.G.[Wen-Guan],
Large-Scale Person Detection and Localization using Overhead Fisheye
Cameras,
ICCV23(19904-19914)
IEEE DOI
2401
BibRef
Das, A.[Arindam],
Das, S.[Sudip],
Sistu, G.[Ganesh],
Horgan, J.[Jonathan],
Bhattacharya, U.[Ujjwal],
Jones, E.[Edward],
Glavin, M.[Martin],
Eising, C.[Ciarán],
Revisiting Modality Imbalance In Multimodal Pedestrian Detection,
ICIP23(1755-1759)
IEEE DOI
2312
BibRef
Shastry, K.N.A.[K.N Ajay],
Teja, K.R.S.[K. Ravi Sri],
Nigam, A.[Aditya],
Arora, C.[Chetan],
Favoring One Among Equals - Not a Good Idea: Many-to-one Matching for
Robust Transformer based Pedestrian Detection,
WACV24(748-757)
IEEE DOI Code:
WWW Link.
2404
Training, Pedestrians, Costs, Codes, Predictive models, Transformers,
Algorithms, Image recognition and understanding, Applications,
Autonomous Driving
BibRef
Shastry, K.N.A.[K. N. Ajay],
Chaudhari, J.[Jayesh],
Thapar, D.[Daksh],
Nigam, A.[Aditya],
Arora, C.[Chetan],
Parts Based Attention for Highly Occluded Pedestrian Detection with
Transformers,
ICIP23(3085-3089)
IEEE DOI Code:
WWW Link.
2312
BibRef
Boretti, C.[Chiara],
Bich, P.[Philippe],
Pareschi, F.[Fabio],
Prono, L.[Luciano],
Rovatti, R.[Riccardo],
Setti, G.[Gianluca],
PEDRo: an Event-based Dataset for Person Detection in Robotics,
EventVision23(4065-4070)
IEEE DOI
2309
BibRef
Khan, A.H.[Abdul Hannan],
Nawaz, M.S.[Mohammed Shariq],
Dengel, A.[Andreas],
Localized Semantic Feature Mixers for Efficient Pedestrian Detection
in Autonomous Driving,
CVPR23(5476-5485)
IEEE DOI
2309
BibRef
Song, X.L.[Xiao-Lin],
Chen, B.H.[Bing-Hui],
Li, P.Y.[Peng-Yu],
He, J.Y.[Jun-Yan],
Wang, B.[Biao],
Geng, Y.F.[Yi-Feng],
Xie, X.[Xuansong],
Zhang, H.G.[Hong-Gang],
Optimal Proposal Learning for Deployable End-to-End Pedestrian
Detection,
CVPR23(3250-3260)
IEEE DOI
2309
BibRef
Wu, W.H.[Wen-Hao],
Wu, S.[Si],
Wong, H.S.[Hau-San],
Unreliability-aware Disentangling for Cross-domain Semi-supervised
Pedestrian Detection,
ACCV22(II:187-203).
Springer DOI
2307
BibRef
Wang, X.T.[Xiao-Tian],
Zhao, L.[Letian],
Wu, W.[Wei],
Jin, X.[Xi],
MCANet: Multiscale Cross-Modality Attention Network for Multispectral
Pedestrian Detection,
MMMod23(I: 41-53).
Springer DOI
2304
BibRef
Song, Z.[Zimiao],
Jin, H.M.[Hong-Mei],
Li, Z.L.[Zhan-Li],
Research on Multiscale Pedestrian Detection Algorithm,
ICIVC22(106-113)
IEEE DOI
2301
Cross layer design, Surveillance, Computational modeling,
Object detection, Feature extraction, Stability analysis,
multiscale feature fusion
BibRef
Deng, J.[Jian],
Li, M.Y.[Ming-Yue],
Chen, Y.X.[Yong-Xin],
Shao, Z.Z.[Zhen-Zhou],
Qu, Y.[Ying],
Guan, Y.[Yong],
Zhang, J.[Jun],
Shi, Z.P.[Zhi-Ping],
Cross-Guided Feature Fusion with Intra-Modality Reweighting for
Multi-Spectral Pedestrian Detection,
ICPR22(4864-4870)
IEEE DOI
2212
Redundancy, Detectors, Feature extraction, Task analysis
BibRef
Cong, P.S.[Pei-Shan],
Zhu, X.G.[Xin-Ge],
Qiao, F.[Feng],
Ren, Y.M.[Yi-Ming],
Peng, X.D.[Xi-Dong],
Hou, Y.N.[Yue-Nan],
Xu, L.[Lan],
Yang, R.G.[Rui-Gang],
Manocha, D.[Dinesh],
Ma, Y.X.[Yue-Xin],
STCrowd:
A Multimodal Dataset for Pedestrian Perception in Crowded Scenes,
CVPR22(19576-19585)
IEEE DOI
2210
Point cloud compression, Annotations, Benchmark testing, Sensors,
Synchronization,
RGBD sensors and analytics
BibRef
Masiero, A.,
Dabove, P.,
di Pietra, V.,
Piragnolo, M.,
Vettore, A.,
Cucchiaro, S.,
Guarnieri, A.,
Tarolli, P.,
Toth, C.,
Gikas, V.,
Perakis, H.,
Chiang, K.W.,
Ruotsalainen, L.M.,
Goel, S.,
Gabela, J.,
A Case Study of Pedestrian Positioning with UWB and UAV Cameras,
ISPRS21(B1-2021: 111-116).
DOI Link
2201
See also UAV UWB Positioning Close to Building Facades: A Case Study.
BibRef
Pahalawatta, K.[Kapila],
Fourie, J.[Jaco],
Potgieter, J.[Johan],
Ascot-Evans, H.[Heath],
Werner, A.[Armin],
Robust human instance segmentation in a challenging forest
environment,
IVCNZ21(1-6)
IEEE DOI
2201
Image segmentation, Navigation, Image color analysis,
Vegetation mapping, Forestry, Thermal sensors, Robot sensing systems
BibRef
Hasan, I.[Irtiza],
Liao, S.C.[Sheng-Cai],
Li, J.P.[Jin-Peng],
Akram, S.U.[Saad Ullah],
Shao, L.[Ling],
Generalizable Pedestrian Detection: The Elephant In The Room,
CVPR21(11323-11332)
IEEE DOI
2111
Training, Pipelines, Detectors, Benchmark testing,
Video surveillance, Distance measurement
BibRef
Wanchaitanawong, N.[Napat],
Tanaka, M.[Masayuki],
Shibata, T.[Takashi],
Okutomi, M.[Masatoshi],
Multi-Modal Pedestrian Detection with Large Misalignment Based on
Modal-Wise Regression and Multi-Modal IoU,
MVA21(1-6)
DOI Link
2109
Electric breakdown, Lighting, Robustness
BibRef
Ge, Z.[Zheng],
Hu, C.Y.[Chu-Yu],
Huang, X.[Xin],
Qiu, B.Q.[Bai-Qiao],
Yoshie, O.[Osamu],
DualBox: Generating BBox Pair with Strong Correspondence via
Occlusion Pattern Clustering and Proposal Refinement,
ICPR21(2097-2102)
IEEE DOI
2105
Pedestrians.
Clustering algorithms, Pattern clustering, Detectors,
Benchmark testing, Prediction algorithms, Non-Maximum-Suppression
BibRef
Fang, P.F.[Peng-Fei],
Ji, P.[Pan],
Zhou, J.[Jieming],
Petersson, L.[Lars],
Harandi, M.[Mehrtash],
Channel Recurrent Attention Networks for Video Pedestrian Retrieval,
ACCV20(VI:427-443).
Springer DOI
2103
BibRef
Yang, X.Y.[Xing-Yi],
Wang, Y.[Yong],
Laganière, R.[Robert],
A Scale-aware Yolo Model for Pedestrian Detection,
ISVC20(II:15-26).
Springer DOI
2103
BibRef
Ghasemi, M.,
Varshosaz, M.,
Pirasteh, S.,
Evaluating Sector Ring Histogram of Oriented Gradients Filter In
Locating Humans Within UAV Images,
ISPRS20(B2:23-27).
DOI Link
2012
BibRef
Zhou, K.L.[Kai-Lai],
Chen, L.S.[Lin-Sen],
Cao, X.[Xun],
Improving Multispectral Pedestrian Detection by Addressing Modality
Imbalance Problems,
ECCV20(XVIII:787-803).
Springer DOI
2012
BibRef
Yang, L.[Lu],
Song, Q.[Qing],
Wang, Z.H.[Zhi-Hui],
Hu, M.J.[Meng-Jie],
Liu, C.[Chun],
Xin, X.S.[Xue-Shi],
Jia, W.H.[Wen-He],
Xu, S.C.[Song-Cen],
Renovating Parsing R-CNN for Accurate Multiple Human Parsing,
ECCV20(XII: 421-437).
Springer DOI
2010
BibRef
Simon, J.[Jules],
Bilodeau, G.A.[Guillaume-Alexandre],
Steele, D.[David],
Mahadik, H.[Harshad],
Color Inference from Semantic Labeling for Person Search in Videos,
ICIAR20(I:139-151).
Springer DOI
2007
BibRef
Vobecký, A.,
Uricár, M.,
Hurych, D.,
Škoviera, R.,
Advanced Pedestrian Dataset Augmentation for Autonomous Driving,
ADW19(2367-2372)
IEEE DOI
2004
pedestrians, pose estimation, occlusions,
autonomous driving applications, people image generation,
autonomous driving
BibRef
Niu, K.,
Huang, Y.,
Wang, L.,
Fusing Two Directions in Cross-Domain Adaption for Real Life Person
Search by Language,
WIDER19(1815-1818)
IEEE DOI
2004
image fusion, learning (artificial intelligence),
natural language processing, neural nets, video surveillance,
Image sentence matching
BibRef
Liu, W.[Wei],
Liao, S.C.[Sheng-Cai],
Ren, W.Q.[Wei-Qiang],
Hu, W.D.[Wei-Dong],
Yu, Y.[Yinan],
High-Level Semantic Feature Detection:
A New Perspective for Pedestrian Detection,
CVPR19(5182-5191).
IEEE DOI
2002
BibRef
Baeck, P.J.,
Lewyckyj, N.,
Beusen, B.,
Horsten, W.,
Pauly, K.,
Drone Based Near Real-time Human Detection With Geographic Localization,
Gi4DM19(49-53).
DOI Link
1912
BibRef
Li, T.,
Wan, W.,
Huang, Y.,
Chen, J.,
Hu, C.,
Ma, Y.,
Improving Human Parsing by Extracting Global Information Using the
Non-Local Operation,
ICIP19(2961-2965)
IEEE DOI
1910
Deep Learning, Human Parsing, Semantic Segmentation, Non-local Operation
BibRef
Fang, L.J.[Liang-Ji],
Zhao, X.[Xu],
Song, X.[Xiao],
Zhang, S.Q.[Shi-Quan],
Yang, M.[Ming],
Putting the Anchors Efficiently:
Geometric Constrained Pedestrian Detection,
ACCV18(V:387-403).
Springer DOI
1906
BibRef
Peng, X.,
Murphey, Y.,
Stent, S.,
Li, Y.,
Zhao, Z.,
Spatial Focal Loss for Pedestrian Detection in Fisheye Imagery,
WACV19(561-569)
IEEE DOI
1904
cameras, learning (artificial intelligence), object detection,
pedestrians, traffic engineering computing, camera system,
Task analysis
BibRef
Tamura, M.,
Horiguchi, S.,
Murakami, T.,
Omnidirectional Pedestrian Detection by Rotation Invariant Training,
WACV19(1989-1998)
IEEE DOI
1904
convolutional neural nets, image processing, object detection,
pedestrians, omnidirectional pedestrian detection,
Transforms
BibRef
Wen, F.[Fang],
Lin, Z.H.[Ze-Hang],
Yang, Z.G.[Zhen-Guo],
Liu, W.Y.[Wen-Yin],
Single-Stage Detector with Semantic Attention for Occluded Pedestrian
Detection,
MMMod19(II:414-425).
Springer DOI
1901
BibRef
Zhang, T.R.[Tai-Ran],
Lang, C.Y.[Cong-Yan],
Xing, J.L.[Jun-Liang],
Realtime Human Segmentation in Video,
MMMod19(II:206-217).
Springer DOI
1901
BibRef
Chavdarova, T.,
Baqué, P.,
Bouquet, S.,
Maksai, A.,
Jose, C.,
Bagautdinov, T.,
Lettry, L.,
Fua, P.,
Van Gool, L.J.,
Fleuret, F.,
WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian
Detection,
CVPR18(5030-5039)
IEEE DOI
1812
Cameras, Benchmark testing,
Computational modeling, Tracking, Synchronization, Detectors
BibRef
Chang, Y.,
Chen, H.,
Chuang, J.,
Liao, I.,
Pedestrian Detection in Aerial Images Using Vanishing Point
Transformation and Deep Learning,
ICIP18(1917-1921)
IEEE DOI
1809
Machine learning, Feature extraction, Transforms, Object detection,
Drones, Image recognition, Computational modeling,
vanishing point transformation
BibRef
Ulutan, O.,
Riggan, B.S.,
Nasrabadi, N.M.,
Manjunath, B.S.,
An Order Preserving Bilinear Model for Person Detection in
Multi-Modal Data,
WACV18(1160-1169)
IEEE DOI
1806
cameras, image fusion, image resolution, neural nets,
object detection, sensor fusion, spatiotemporal phenomena, vectors,
Visualization
BibRef
Krams, O.,
Kiryati, N.,
People detection in top-view fisheye imaging,
AVSS17(1-6)
IEEE DOI
1806
Jacobian matrices, calibration, cameras, feature extraction,
image reconstruction, image sensors, lenses, object detection,
Standards
BibRef
Kniaz, V.V.,
Fedorenko, V.V.,
An Algorithm for Pedestrian Detection in Multispectral Image Sequences,
PTVSBB17(73-77).
DOI Link
1805
BibRef
Zhang, H.,
Hu, X.,
Zhuo, L.,
Zhang, J.,
Pedestrian Detection Based on Imbalance Prior for Surveillance Video,
DICTA17(1-7)
IEEE DOI
1804
edge detection, feature extraction, image classification,
image colour analysis, image resolution, pedestrians,
Surveillance
BibRef
Williams, J.,
Carneiro, G.,
Suter, D.,
Region of Interest Autoencoders with an Application to Pedestrian
Detection,
DICTA17(1-8)
IEEE DOI
1804
image reconstruction,
learning (artificial intelligence), object detection,
Training
BibRef
García-Martín, A.,
San Miguel, J.C.,
Adaptive people detection based on cross-correlation maximization,
ICIP17(3385-3389)
IEEE DOI
1803
Correlation, Detectors, Mutual information, Power capacitors,
Runtime, Standards, Training, Detector adaptation,
Thresholds
BibRef
Yan, Y.,
Xu, M.,
Smith, J.S.,
Multiview pedestrian localisation via a prime candidate chart based
on occupancy likelihoods,
ICIP17(2334-2338)
IEEE DOI
1803
Benchmark testing, Cameras, Color, Phantoms,
Robustness, Transmission line matrix methods, image fusion,
visual surveillance
BibRef
Chien, J.T.,
Chou, C.J.,
Chen, D.J.,
Chen, H.T.,
Detecting Nonexistent Pedestrians,
CVRoads17(182-189)
IEEE DOI
1802
Head, Image segmentation, Training, Training data
BibRef
Zhou, H.Y.,
Gao, B.B.,
Wu, J.,
Adaptive Feeding: Achieving Fast and Accurate Detections by
Adaptively Combining Object Detectors,
ICCV17(3525-3533)
IEEE DOI
1802
image classification, object detection, AF classifier,
Adaptive Feeding, Caltech Pedestrian dataset, MS COCO dataset,
Real-time systems
BibRef
Gabriel, E.[Eric],
Schramm, H.[Hauke],
Meyer, C.[Carsten],
Analysis of the Discriminative Generalized Hough Transform for
Pedestrian Detection,
CIAP17(II:104-115).
Springer DOI
1711
BibRef
Eldesokey, A.[Abdelrahman],
Felsberg, M.[Michael],
Khan, F.S.[Fahad Shahbaz],
Ellipse Detection for Visual Cyclists Analysis 'In the Wild',
CAIP17(I: 319-331).
Springer DOI
1708
BibRef
Chandran, A.K.,
Subramaniam, A.,
Wong, W.C.,
Yang, J.,
Chaturvedi, K.A.,
A PTZ camera based people-occupancy estimation system (PCBPOES),
MVA17(145-148)
DOI Link
1708
Cameras, Head, Lighting, Magnetic heads, Probabilistic logic, Retina,
Support vector machines.
BibRef
Wang, H.,
Gu, Y.,
Kamijo, S.,
Pedestrian positioning in urban city with the aid of Google maps
street view,
MVA17(456-459)
DOI Link
1708
Buildings, Cameras, Google, Image matching, Meters, Urban, areas
BibRef
Wang, D.[Dan],
Zhang, C.Y.[Chong-Yang],
Cheng, H.[Hao],
Shang, Y.F.[Yan-Feng],
Mei, L.[Lin],
SPID: Surveillance Pedestrian Image Dataset and Performance Evaluation
for Pedestrian Detection,
BEST16(III: 463-477).
Springer DOI
1704
Dataset, Pedestrians.
BibRef
Cao, C.,
Wang, Y.[Yu],
Kato, J.[Jien],
Zhang, G.W.[Guan-Wen],
Mase, K.J.[Ken-Ji],
Solving Occlusion Problem in Pedestrian Detection by Constructing
Discriminative Part Layers,
WACV17(91-99)
IEEE DOI
1609
Data mining, Detectors, Feature extraction, Pipelines, Robustness,
Training, Visualization
BibRef
Kokubo, Y.,
Wang, Y.[Yu],
Kato, J.[Jien],
Zhang, G.W.[Guan-Wen],
Mase, K.J.[Ken-Ji],
Add-On Strategies for Fine-Grained Pedestrian Classification,
DICTA16(1-6)
IEEE DOI
1701
Feature extraction
BibRef
Lee, D.[Donghoon],
Cha, G.[Geonho],
Yang, M.H.[Ming-Hsuan],
Oh, S.H.[Song-Hwai],
Individualness and Determinantal Point Processes for Pedestrian
Detection,
ECCV16(VI: 330-346).
Springer DOI
1611
BibRef
Boui, M.,
Hadj-Abdelkader, H.[Hicham],
Ababsa, F.E.,
Bouyakhf, E.H.,
New approach for human detection in spherical images,
ICIP16(604-608)
IEEE DOI
1610
Adaptation models
BibRef
Zhang, S.,
Zhu, Q.,
Roy-Chowdhury, A.,
Adaptive algorithm selection, with applications in pedestrian
detection,
ICIP16(3768-3772)
IEEE DOI
1610
Algorithm design and analysis
BibRef
Correia, A.J.L.,
Schwartz, W.R.,
Oblique random forest based on partial least squares applied to
pedestrian detection,
ICIP16(2931-2935)
IEEE DOI
1610
Computer vision
BibRef
Errami, M.,
Rziza, M.[Mohammed],
Improving Pedestrian Detection Using Support Vector Regression,
CGiV16(156-160)
IEEE DOI
1608
Haar transforms
BibRef
Rehder, E.,
Kloeden, H.,
Goal-Directed Pedestrian Prediction,
CVRoads15(139-147)
IEEE DOI
1602
Context
BibRef
Toca, C.[Cosmin],
Ciuc, M.[Mihai],
Patrascu, C.[Carmen],
Normalized Autobinomial Markov Channels For Pedestrian Detection,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Yang, Y.[Yi],
Wang, Z.H.[Zhen-Hua],
Wu, F.C.[Fu-Chao],
Exploring Prior Knowledge for Pedestrian Detection,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Xu, P.[Philippe],
Davoine, F.[Franck],
Denoeux, T.[Thierry],
Evidential combination of pedestrian detectors,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
Abid, N.[Nesrine],
Loukil, K.[Kais],
Ayedi, W.[Walid],
Ammari, A.C.[Ahmed Chiheb],
Abid, M.[Mohamed],
Optimized Parallel Model of Covariance Based Person Detection,
CIAP15(II:287-298).
Springer DOI
1511
BibRef
Arana-Daniel, N.[Nancy],
Cibrian-Decena, I.[Isabel],
Recognition of Non-pedestrian Human Forms Through Locally Weighted
Descriptors,
CIARP15(751-759).
Springer DOI
1511
BibRef
Xu, R.[Rong],
Ueno, S.[Satoshi],
Kobayashi, T.[Tatsuya],
Makibuchi, N.[Naoya],
Naito, S.[Sei],
Human Area Refinement for Human Detection,
CIAP15(II:130-141).
Springer DOI
1511
BibRef
Ma, Z.[Zheng],
Yu, L.[Lei],
Chan, A.B.[Antoni B.],
Small instance detection by integer programming on object density
maps,
CVPR15(3689-3697)
IEEE DOI
1510
BibRef
Jiang, Y.S.[Yun-Sheng],
Ma, J.W.[Jin-Wen],
Combination features and models for human detection,
CVPR15(240-248)
IEEE DOI
1510
BibRef
Hosang, J.[Jan],
Omran, M.[Mohamed],
Benenson, R.[Rodrigo],
Schiele, B.[Bernt],
Taking a deeper look at pedestrians,
CVPR15(4073-4082)
IEEE DOI
1510
BibRef
Becker, S.[Stefan],
Kieritz, H.[Hilke],
Hübner, W.[Wolfgang],
Arens, M.[Michael],
On the Benefit of State Separation for Tracking in Image Space with an
Interacting Multiple Model Filter,
ICISP16(3-11).
WWW Link.
1606
BibRef
Becker, S.[Stefan],
Hubner, W.[Wolfgang],
Arens, M.[Michael],
Annotation driven MAP search space estimation for sliding-window
based person detection,
MVA15(430-434)
IEEE DOI
1507
Cameras
BibRef
Ma, P.[Puhao],
Sun, L.[Lei],
Ai, H.Z.[Hai-Zhou],
Sakai, S.[Shun],
Boosted pedestrian detector adaptation in specific scenes,
MVA15(230-233)
IEEE DOI
1507
Detectors
BibRef
Gil, J.I.[Jong-In],
Mahmoudpour, S.,
Kim, M.,
Automatic light control system using fish-eye lens camera,
FCV15(1-3)
IEEE DOI
1506
human detection.
object detection
BibRef
Blondel, P.,
Potelle, A.,
Pégard, C.,
Lozano, R.,
Lara, D.,
Dynamic collaboration of far-infrared and visible spectrum for human
detection,
ICPR16(698-703)
IEEE DOI
1705
BibRef
Earlier: A1, A2, A3, A4, Only:
Fast and viewpoint robust human detection in uncluttered environments,
VCIP14(522-525)
IEEE DOI
1504
Cameras, Collaboration, Detectors, Feature extraction, Optimization,
Stereo image processing, Synchronization.
BibRef
Hwang, S.[Soonmin],
Oh, T.H.[Tae-Hyun],
Kweon, I.S.[In So],
A Two Phase Approach for Pedestrian Detection,
IVVT14(459-474).
Springer DOI
1504
BibRef
Wang, X.[Xiao],
Chen, J.[Jun],
Fang, W.H.[Wen-Hua],
Liang, C.[Chao],
Zhang, C.J.[Chun-Jie],
Hu, R.M.[Rui-Min],
Pedestrian detection from salient regions,
ICIP14(2423-2426)
IEEE DOI
1502
Bayes methods
BibRef
Zhang, X.G.[Xing-Guo],
Chen, G.Y.[Guo-Yue],
Saruta, K.[Kazuki],
Terata, Y.[Yuki],
A Simple Visual Words Selection Strategy for Pedestrian Detection,
ISVC14(I: 658-667).
Springer DOI
1501
BibRef
Tani, Y.[Yuta],
Hotta, K.[Kazuhiro],
Robust Human Detection to Pose and Occlusion Using Bag-of-Words,
ICPR14(4376-4381)
IEEE DOI
1412
Accuracy
BibRef
Nilsson, J.[Jonas],
Andersson, P.[Patrik],
Gu, I.Y.H.[Irene Y.H.],
Fredriksson, J.[Jonas],
Pedestrian Detection Using Augmented Training Data,
ICPR14(4548-4553)
IEEE DOI
1412
Data models
BibRef
de Smedt, F.[Floris],
Puttemans, S.,
Goedemé, T.[Toon],
How to reach top accuracy for a visual pedestrian warning system from
a car?,
IPTA16(1-6)
IEEE DOI
1703
alarm systems
BibRef
de Smedt, F.[Floris],
van Beeck, K.[Kristof],
Tuytelaars, T.[Tinne],
Goedeme, T.[Toon],
The Combinator: Optimal Combination of Multiple Pedestrian Detectors,
ICPR14(3522-3527)
IEEE DOI
1412
Accuracy
BibRef
Bartoli, F.[Federico],
Lisanti, G.[Giuseppe],
Karaman, S.[Svebor],
Bagdanov, A.D.[Andrew D.],
del Bimbo, A.[Alberto],
Unsupervised Scene Adaptation for Faster Multi-scale Pedestrian
Detection,
ICPR14(3534-3539)
IEEE DOI
1412
Accuracy
BibRef
Frejlichowski, D.[Dariusz],
Gosciewska, K.[Katarzyna],
Forczmanski, P.[Pawel],
Hofman, R.[Radoslaw],
Human Detection for a Video Surveillance Applied in the 'SmartMonitor'
System,
ICCVG14(220-227).
Springer DOI
1410
BibRef
Sangineto, E.[Enver],
Statistical and Spatial Consensus Collection for Detector Adaptation,
ECCV14(III: 456-471).
Springer DOI
1408
Adaptation of pedestrian detectors toward specific scenarios.
BibRef
Sager, H.[Hisham],
Hoff, W.A.[William A.],
Pedestrian detection in low resolution videos,
WACV14(668-673)
IEEE DOI
1406
Detectors
BibRef
Tao, J.L.[Jun-Li],
Klette, R.,
Part-Based RDF for Direction Classification of Pedestrians, and a
Benchmark,
IVVT14(418-432).
Springer DOI
1504
BibRef
Earlier:
Integrated Pedestrian and Direction Classification Using a Random
Decision Forest,
AutoDrive13(230-237)
IEEE DOI
1403
behavioural sciences computing
BibRef
Barbosa-Anda, F.R.,
Lerasle, F.[Frédéric],
Briand, C.,
Mekonnen, A.A.[Alhayat Ali],
Soft-Cascade Learning with Explicit Computation Time Considerations,
WACV18(1234-1243)
IEEE DOI
1806
computational complexity, image classification,
learning (artificial intelligence), object detection,
Tuning
BibRef
Rujikietgumjorn, S.[Sitapa],
Collins, R.T.[Robert T.],
Optimized Pedestrian Detection for Multiple and Occluded People,
CVPR13(3690-3697)
IEEE DOI
1309
BibRef
Hosang, J.[Jan],
Benenson, R.[Rodrigo],
Schiele, B.[Bernt],
How good are detection proposals, really?,
BMVC14(xx-yy).
HTML Version.
1410
Object detectors start with detection proposals.
BibRef
Benenson, R.[Rodrigo],
Mathias, M.[Markus],
Tuytelaars, T.[Tinne],
Van Gool, L.J.[Luc J.],
Seeking the Strongest Rigid Detector,
CVPR13(3666-3673)
IEEE DOI
1309
objects detection; pedestrian detection
BibRef
Benenson, R.[Rodrigo],
Mathias, M.[Markus],
Timofte, R.[Radu],
Van Gool, L.J.[Luc J.],
Fast Stixel Computation for Fast Pedestrian Detection,
CVVT12(III: 11-20).
Springer DOI
1210
BibRef
And:
Pedestrian detection at 100 frames per second,
CVPR12(2903-2910).
IEEE DOI
1208
BibRef
Taiana, M.[Matteo],
Nascimento, J.C.[Jacinto C.],
Bernardino, A.[Alexandre],
An Improved Labelling for the INRIA Person Data Set for Pedestrian
Detection,
IbPRIA13(286-295).
Springer DOI
1307
BibRef
Wang, J.Q.[Jian-Qing],
Wang, M.[Min],
Qiao, H.[Hong],
Keane, J.,
Oriented Gradient Context for pedestrian detection,
ICARCV12(1142-1147).
IEEE DOI
1304
BibRef
Wang, L.[Li],
Chan, K.L.[Kap Luk],
Wang, G.[Gang],
Human Detection with Occlusion Handling by Over-Segmentation and
Clustering on Foreground Regions,
CDF12(II:197-208).
Springer DOI
1304
BibRef
Hao, P.Y.[Peng-Yi],
Kamata, S.I.[Sei-Ichiro],
An efficient video retrieval scheme based on facial signatures,
ICIP13(2699-2703)
IEEE DOI
1402
BibRef
Earlier:
Unsupervised people organization and its application on individual
retrieval from videos,
ICPR12(2001-2004).
WWW Link.
1302
Linear discriminant analysis;Signature;Video retrieval
BibRef
Ahmed, I.[Imran],
Carter, J.N.[John N.],
A robust person detector for overhead views,
ICPR12(1483-1486).
WWW Link.
1302
BibRef
Tasson, D.,
Montagnini, A.,
Marzotto, R.,
Farenzena, M.,
Cristani, M.,
FPGA-based pedestrian detection under strong distortions,
ECVW15(65-70)
IEEE DOI
1510
Cameras
BibRef
Martelli, S.[Samuele],
Tosato, D.[Diego],
Cristani, M.[Marco],
Murino, V.[Vittorio],
Fast FPGA-based architecture for pedestrian detection based on
covariance matrices,
ICIP11(389-392).
IEEE DOI
1201
BibRef
Nodari, A.[Angelo],
Vanetti, M.[Marco],
Gallo, I.[Ignazio],
Digital privacy: Replacing pedestrians from Google Street View images,
ICPR12(2889-2893).
WWW Link.
1302
BibRef
Koyama, T.[Tatsuya],
Nakashima, Y.[Yuta],
Babaguchi, N.[Noboru],
Markov random field-based real-time detection of intentionally-captured
persons,
ICIP12(1377-1380).
IEEE DOI
1302
BibRef
Garcia-Martin, A.[Alvaro],
Cavallaro, A.[Andrea],
Martinez, J.M.[Jose M.],
People-background segmentation with unequal error cost,
ICIP12(157-160).
IEEE DOI
1302
BibRef
Huang, P.J.[Po-Jui],
Chen, D.Y.[Duan-Yu],
Robust wheelchair pedestrian detection using sparse representation,
VCIP12(1-5).
IEEE DOI
1302
BibRef
Tang, D.H.[Dan-Hang],
Liu, Y.[Yang],
Kim, T.K.[Tae-Kyun],
Fast Pedestrian Detection by Cascaded Random Forest with Dominant
Orientation Templates,
BMVC12(58).
DOI Link
1301
BibRef
Ladický, L.[Lubor],
Torr, P.H.S.[Philip H.S.],
Zisserman, A.[Andrew],
Latent SVMs for Human Detection with a Locally Affine Deformation Field,
BMVC12(10).
DOI Link
1301
BibRef
Evans, M.[Murray],
Li, L.Z.[Long-Zhen],
Ferryman, J.M.[James M.],
Suppression of Detection Ghosts in Homography Based Pedestrian
Detection,
AVSS12(31-36).
IEEE DOI
1211
BibRef
Kamberov, G.[George],
Burlick, M.[Matt],
Karydas, L.[Lazaros],
Koteoglou, O.[Olga],
Scar: Dynamic Adaptation for Person Detection and Persistence Analysis
in Unconstrained Videos,
ISVC12(II: 176-187).
Springer DOI
1209
BibRef
Ding, Y.Y.[Yuan-Yuan],
Xiao, J.[Jing],
Contextual boost for pedestrian detection,
CVPR12(2895-2902).
IEEE DOI
1208
BibRef
Munaro, M.[Matteo],
Cenedese, A.[Angelo],
Scene specific people detection by simple human interaction,
HICV11(1250-1255).
IEEE DOI
1201
BibRef
Nguyen, D.T.[Duc Thanh],
A Novel Chamfer Template Matching Method Using Variational Mean Field,
CVPR14(2425-2432)
IEEE DOI
1409
Chamfer template matching; object detection; variational mean field
BibRef
Nguyen, D.T.[Duc Thanh],
Ogunbona, P.[Philip],
Li, W.Q.[Wan-Qing],
Detecting humans under occlusion using variational mean field method,
ICIP11(2049-2052).
IEEE DOI
1201
BibRef
Migniot, C.[Cyrille],
Bertolino, P.[Pascal],
Chassery, J.M.[Jean-Marc],
Automatic people segmentation with a template-driven graph cut,
ICIP11(3149-3152).
IEEE DOI
1201
BibRef
Wu, J.C.[Jin-Chen],
Chen, W.[Wei],
Huang, K.Q.[Kai-Qi],
Tan, T.N.[Tie-Niu],
Partial Least Squares based subwindow search for pedestrian detection,
ICIP11(3565-3568).
IEEE DOI
1201
BibRef
Chen, X.T.[Xiao-Tang],
Huang, K.Q.[Kai-Qi],
Tan, T.N.[Tie-Niu],
Direction-based stochastic matching for pedestrian recognition in
non-overlapping cameras,
ICIP11(2065-2068).
IEEE DOI
1201
BibRef
El Guebaly, T.[Tarek],
Bouguila, N.[Nizar],
A nonparametric Bayesian approach for enhanced pedestrian detection and
foreground segmentation,
OTCBVS11(21-26).
IEEE DOI
1106
BibRef
Kim, D.H.[Dae-Hwan],
Kim, Y.[Yeonho],
Kim, D.J.[Dai-Jin],
Separating Occluded Humans by Bayesian Pixel Classifier with
Re-weighted Posterior Probability,
ACIVS11(543-553).
Springer DOI
1108
BibRef
Barnich, O.[Olivier],
Piérard, S.[Sébastien],
van Droogenbroeck, M.[Marc],
A Virtual Curtain for the Detection of Humans and Access Control,
ACIVS10(II: 98-109).
Springer DOI
1012
BibRef
Yu, J.[Jie],
Farin, D.[Dirk],
Kruger, C.[Christof],
Schiele, B.[Bernt],
Improving person detection using synthetic training data,
ICIP10(3477-3480).
IEEE DOI
1009
BibRef
Middleton, L.[Lee],
Snowdon, J.R.[James R.],
Histogram of confidences for person detection,
ICIP10(1841-1844).
IEEE DOI
1009
BibRef
Garcia-Martin, A.,
Martinez, J.M.,
Robust Real Time Moving People Detection in Surveillance Scenarios,
AVSS10(241-247).
IEEE DOI
1009
BibRef
Shen, J.L.[Jia-Li],
Yan, W.Q.[Wei-Qi],
Miller, P.,
Zhou, H.Y.[Hui-Yu],
Human Localization in a Cluttered Space Using Multiple Cameras,
AVSS10(85-90).
IEEE DOI
1009
BibRef
Atienza-Vanacloig, V.[Vicente],
Rosell-Ortega, J.[Juan],
Andreu-Garcia, G.[Gabriela],
Valiente-Gonalez, J.M.[Jose Miguel],
Locating People in Images by Optimal Cue Integration,
ICPR10(1804-1807).
IEEE DOI
1008
BibRef
Heimonen, T.A.[Teuvo Antero],
Heikkila, J.[Janne],
A Human Detection Framework for Heavy Machinery,
ICPR10(416-419).
IEEE DOI
1008
BibRef
Ma, W.H.[Wen-Hua],
He, P.[Peng],
Huang, L.[Lei],
Liu, C.P.[Chang-Ping],
Context Inspired Pedestrian Detection in Far-Field Videos,
ICPR10(3009-3012).
IEEE DOI
1008
BibRef
Hong, X.P.[Xiao-Peng],
Chang, H.[Hong],
Chen, X.L.[Xi-Lin],
Gao, W.[Wen],
Boosted Sigma Set for Pedestrian Detection,
ICPR10(3017-3020).
IEEE DOI
1008
See also Sigma Set: A small second order statistical region descriptor.
BibRef
Cai, Y.H.[Ying-Hao],
Takala, V.[Valtteri],
Pietikainen, M.[Matti],
Matching Groups of People by Covariance Descriptor,
ICPR10(2744-2747).
IEEE DOI
1008
BibRef
Simonnet, D.[Damien],
Velastin, S.A.[Sergio A.],
Pedestrian detection based on adaboost algorithm with a
pseudo-calibrated camera,
IPTA10(54-59).
IEEE DOI
1007
BibRef
Flores, A.[Arturo],
Belongie, S.J.[Serge J.],
Removing pedestrians from Google street view images,
IWMV10(53-58).
IEEE DOI
1006
BibRef
Ott, P.[Patrick],
Everingham, M.[Mark],
Implicit color segmentation features for pedestrian and object
detection,
ICCV09(723-730).
IEEE DOI
0909
BibRef
Pang, J.B.[Jun-Biao],
Huang, Q.M.[Qing-Ming],
Jiang, S.Q.[Shu-Qiang],
Wu, Z.P.[Zhi-Peng],
Transfer pedestrian detector towards view-adaptiveness and efficiency,
ObjectEvent09(609-616).
IEEE DOI
0910
BibRef
Liao, C.T.[Chia-Te],
Lai, S.H.[Shang-Hong],
Wang, W.H.[Wen-Hao],
A hierarchical image kernel with application to pedestrian
identification for video surveillance,
ICIP09(1125-1128).
IEEE DOI
0911
BibRef
Yu, X.G.[Xin-Guo],
Dong, L.[Li],
Li, L.Y.[Li-Yuan],
Hoe, J.K.E.[Jerry Kah Eng],
Lift-button detection and recognition for service robot in buildings,
ICIP09(313-316).
IEEE DOI
0911
BibRef
Li, L.Y.[Li-Yuan],
Hoe, J.K.E.[Jerry Kah Eng],
Yan, S.C.[Shui-Cheng],
Yu, X.G.[Xin-Guo],
ML-fusion based multi-model human detection and tracking for robust
human-robot interfaces,
WACV09(1-8).
IEEE DOI
0912
BibRef
Bolme, D.S.[David S.],
Beveridge, J.R.[J. Ross],
Draper, B.A.[Bruce A.],
Lui, Y.M.[Yui Man],
Visual object tracking using adaptive correlation filters,
CVPR10(2544-2550).
IEEE DOI
1006
BibRef
Earlier: A1, A4, A3, A2:
Simple real-time human detection using a single correlation filter,
PETS-Winter09(1-8).
IEEE DOI
0912
BibRef
Lai, J.,
Ford, J.J.,
O'Shea, P.,
Walker, R.,
Hidden Markov Model Filter Banks for Dim Target Detection from Image
Sequences,
DICTA08(312-319).
IEEE DOI
0812
BibRef
Yu, L.P.[Li-Ping],
Yao, W.T.[Wen-Tao],
Pedestrian Detection Fusion Method Based on Mean Shift,
ICMV09(204-207).
IEEE DOI
0912
BibRef
Rapus, M.[Martin],
Munder, S.[Stefan],
Baratoff, G.[Gregory],
Denzler, J.[Joachim],
Pedestrian Detection by Probabilistic Component Assembly,
DAGM09(91-100).
Springer DOI
0909
BibRef
Pang, J.B.[Jun-Biao],
Huang, Q.M.[Qing-Ming],
Jiang, S.Q.[Shu-Qiang],
Multiple Instance Boost Using Graph Embedding Based Decision Stump for
Pedestrian Detection,
ECCV08(IV: 541-552).
Springer DOI
0810
BibRef
Lu, H.C.[Hu-Chuan],
Jia, C.H.[Chun-Hua],
Zhang, R.J.[Rui-Juan],
An effective method for detection and segmentation of the body of human
in the view of a single stationary camera,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Thome, N.[Nicolas],
Ambellouis, S.[Sebastien],
A bottom-up, view-point invariant human detector,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Park, J.M.[Jung-Me],
Luo, Y.[Yun],
Wang, H.X.[Hao-Xing],
Murphey, Y.L.[Yi L.],
Pedestrian detection by modeling local convex shape features,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Leyrit, L.[Laetitia],
Chateau, T.[Thierry],
Tournayre, C.[Christophe],
Lapreste, J.T.[Jean-Thierry],
Visual pedestrian recognition in weak classifier space using nonlinear
parametric models,
ICIP08(2392-2395).
IEEE DOI
0810
BibRef
Abramson, Y.,
Steux, B.,
Hardware-friendly pedestrian detection and impact prediction,
IVS04(590-595).
IEEE DOI
0411
BibRef
Colombo, A.[Alberto],
Orwell, J.[James],
Velastin, S.A.[Sergio A.],
Colour Constancy Techniques for Re-Recognition of Pedestrians from
Multiple Surveillance Cameras,
M2SFA208(xx-yy).
0810
BibRef
Zhang, C.[Cha],
Hamid, R.[Raffay],
Zhang, Z.Y.[Zheng-You],
Taylor expansion based classifier adaptation:
Application to person detection,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Duan, G.Q.[Gen-Quan],
Ai, H.Z.[Hai-Zhou],
Lao, S.H.[Shi-Hong],
Human Detection in Video over Large Viewpoint Changes,
ACCV10(II: 683-696).
Springer DOI
1011
BibRef
And:
A Structural Filter Approach to Human Detection,
ECCV10(VI: 238-251).
Springer DOI
1009
BibRef
Gao, W.[Wei],
Ai, H.Z.[Hai-Zhou],
Lao, S.H.[Shi-Hong],
Adaptive Contour Features in oriented granular space for human
detection and segmentation,
CVPR09(1786-1793).
IEEE DOI
0906
BibRef
Hou, C.[Cong],
Ai, H.Z.[Hai-Zhou],
Lao, S.H.[Shi-Hong],
Multiview Pedestrian Detection Based on Vector Boosting,
ACCV07(I: 210-219).
Springer DOI
0711
See also High-Performance Rotation Invariant Multiview Face Detection.
BibRef
Chen, Y.T.[Yu-Ting],
Chen, C.S.[Chu-Song],
A Cascade of Feed-Forward Classifiers for Fast Pedestrian Detection,
ACCV07(I: 905-914).
Springer DOI
0711
BibRef
Schulz, W.[Wolfgang],
Enzweiler, M.[Markus],
Ehlgen, T.[Tobias],
Pedestrian Recognition from a Moving Catadioptric Camera,
DAGM07(456-465).
Springer DOI
0709
BibRef
Shet, V.D.[Vinay D.],
Neumann, J.[Jan],
Ramesh, V.[Visvanathan],
Davis, L.S.[Larry S.],
Bilattice-based Logical Reasoning for Human Detection,
CVPR07(1-8).
IEEE DOI
0706
BibRef
Gallagher, A.C.[Andrew C.],
Blose, A.C.[Andrew C.],
Chen, T.H.[Tsu-Han],
Jointly Estimating Demographics and Height with a Calibrated Camera,
ICCV09(1187-1194).
IEEE DOI
0909
See also ground truth based vanishing point detection algorithm, A.
BibRef
Gallagher, A.C.[Andrew C.],
Chen, T.H.[Tsu-Han],
Using a Markov Network to Recognize People in Consumer Images,
ICIP07(IV: 489-492).
IEEE DOI
0709
BibRef
And:
Using Group Prior to Identify People in Consumer Images,
SLAM07(1-8).
IEEE DOI
0706
BibRef
Feris, R.S.[Rogerio S.],
Tian, Y.L.[Ying-Li],
Hampapur, A.[Arun],
Capturing People in Surveillance Video,
VS07(1-8).
IEEE DOI
0706
BibRef
Parikh, D.[Devi],
Zitnick, C.L.[C. Lawrence],
Finding the weakest link in person detectors,
CVPR11(1425-1432).
IEEE DOI
1106
BibRef
Sivic, J.,
Zitnick, C.L.,
Szeliski, R.S.[Richard S.],
Finding people in repeated shots of the same scene,
BMVC06(III:909).
PDF File.
0609
BibRef
Davis, L.S.[Larry S.],
Segmenting people in small groups,
VSSN06(1-2).
WWW Link.
0701
BibRef
Scotti, G.,
Cuocolo, A.,
Coelho, C.,
Marchesotti, L.,
A Novel Pedestrian Classification Algorithm for a High Definition Dual
Camera 360 Degrees Surveillance System,
ICIP05(III: 880-883).
IEEE DOI
0512
BibRef
Zhao, L.[Liang],
Davis, L.S.[Larry S.],
Closely Coupled Object Detection and Segmentation,
ICCV05(I: 454-461).
IEEE DOI
0510
BibRef
And:
Segmentation and Appearance Model Building from an Image Sequence,
ICIP05(I: 321-324).
IEEE DOI
0512
Link detection and segmentation, not separate tasks.
BibRef
Castillo, C.[Carlos],
Chang, C.[Carolina],
An Approach to Vision-Based Person Detection in Robotic Applications,
IbPRIA05(I:209).
Springer DOI
0509
BibRef
Liu, Z.Y.[Zong-Yi],
Sarkar, S.[Sudeep],
Challenges in Segmentation of Human Forms in Outdoor Video,
PercOrg04(43).
IEEE DOI
0502
BibRef
Owechko, Y.,
Medasani, S.,
A Swarm-Based Volition/Attention Framework for Object Recognition,
AttenPerf05(III: 91-91).
IEEE DOI
0507
BibRef
Lombardi, P.,
Zavidovique, B.,
A context-dependent vision system for pedestrian detection,
IVS04(578-583).
IEEE DOI
0411
BibRef
And:
Architectural design issues for bayesian contextual vision,
ICPR04(I: 753-756).
IEEE DOI
0409
BibRef
Dante, A.,
Brookes, M.,
Constantinides, A.G.,
Robust multi-body segmentation,
BMVC03(xx-yy).
HTML Version.
0409
BibRef
Ramoser, H.,
Schlogl, T.,
Beleznai, C.,
Winter, M.,
Bischof, H.,
Shape-based detection of humans for video surveillance applications,
ICIP03(III: 1013-1016).
IEEE DOI
0312
BibRef
Lefee, D.,
Mousset, S.,
Bertozzi, M.,
Bensrhair, A.,
Cooperation of passive vision systems in detection and tracking of
pedestrians,
IVS04(768-773).
IEEE DOI
0411
See also Vehicle Detection by Means of Stereo Vision-Based Obstacles Features Extraction and Monocular Pattern Analysis.
BibRef
Sprague, N.,
Luo, J.B.[Jie-Bo],
Clothed people detection in still images,
ICPR02(III: 585-589).
IEEE DOI
0211
BibRef
Vendrig, J.[Jeroen],
Worring, M.[Marcel],
Multimodal Person Identification in Movies,
CIVR02(175-185).
Springer DOI
0208
BibRef
Utsumi, A.,
Tetsutani, N.,
Human detection using geometrical pixel value structures,
AFGR02(34-39).
IEEE DOI
0206
BibRef
Pujol, A.,
Lumbreras, F.,
Varona, X.,
Villanueva, J.J.[Juan J.],
Locating People in Indoor Scenes for Real Applications,
ICPR00(Vol IV: 632-635).
IEEE DOI
0009
BibRef
Lee, M.S.[Mi-Suen],
Detecting People in Cluttered Indoor Scenes,
CVPR00(I: 804-809).
IEEE DOI
0005
BibRef
Steffens, J.B.[Johannes Bernhard],
Elagin, E.V.[Egor Valerievich],
Neven, H.[Hartmut],
PersonSpotter: Fast and Robust System for Human Detection, Tracking
and Recognition,
AFGR98(516-521).
IEEE DOI
BibRef
9800
Kuno, Y.,
Watanabe, T.,
Shimosakoda, Y.,
Nakagawa, S.,
Automated Detection of Human for Visual Surveillance System,
ICPR96(III: 865-869).
IEEE DOI
9608
(Kanasi Laboratory, J)
BibRef
Kosugi, M.[Makoto],
Yamashita, K.[Kouji],
Person identification system based on a trapezoid pyramid architecture
of a gray-level image,
CIAP97(II: 501-508).
Springer DOI
9709
BibRef
Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Learning, Neural Nets for Human Detection, People Detection, Pedestrians .