Tuzel, O.[Oncel],
Porikli, F.M.[Fatih M.],
Meer, P.[Peter],
Pedestrian Detection via Classification on Riemannian Manifolds,
PAMI(30), No. 10, October 2008, pp. 1713-1727.
IEEE DOI
0810
BibRef
Earlier:
Human Detection via Classification on Riemannian Manifolds,
CVPR07(1-8).
IEEE DOI
Award, CVPR, HM.
0706
BibRef
Tuzel, O.[Oncel],
Porikli, F.M.[Fatih M.],
Meer, P.[Peter],
Learning on lie groups for invariant detection and tracking,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Earlier:
Region Covariance: A Fast Descriptor for Detection and Classification,
ECCV06(II: 589-600).
Springer DOI
0608
BibRef
Earlier: A2, A1, A3:
Covariance Tracking using Model Update Based on Lie Algebra,
CVPR06(I: 728-735).
IEEE DOI
0606
BibRef
Porikli, F.M.,
Tuzel, O.[Oncel],
Fast Construction of Covariance Matrices for Arbitrary Size Image
Windows,
ICIP06(1581-1584).
IEEE DOI
0610
BibRef
Liang, F.[Feidie],
Tang, S.[Sheng],
Zhang, Y.D.[Yong-Dong],
Xu, Z.X.[Zuo-Xin],
Li, J.T.[Jin-Tao],
Pedestrian detection based on sparse coding and transfer learning,
MVA(25), No. 7, October 2014, pp. 1697-1709.
Springer DOI
1410
BibRef
Ouyang, W.L.[Wan-Li],
Zeng, X.Y.[Xing-Yu],
Wang, X.G.[Xiao-Gang],
Single-Pedestrian Detection Aided by Two-Pedestrian Detection,
PAMI(37), No. 9, September 2015, pp. 1875-1889.
IEEE DOI
1508
Context
BibRef
Ouyang, W.L.[Wan-Li],
Zeng, X.Y.[Xing-Yu],
Wang, X.G.[Xiao-Gang],
Learning Mutual Visibility Relationship for Pedestrian Detection with a
Deep Model,
IJCV(120), No. 1, October 2016, pp. 14-27.
Springer DOI
1609
BibRef
Earlier: A2, A1, A3:
Multi-stage Contextual Deep Learning for Pedestrian Detection,
ICCV13(121-128)
IEEE DOI
1403
BibRef
And: A1, A2, A3:
Modeling Mutual Visibility Relationship in Pedestrian Detection,
CVPR13(3222-3229)
IEEE DOI
1309
BibRef
And: A1, A3, Only:
Single-Pedestrian Detection Aided by Multi-pedestrian Detection,
CVPR13(3198-3205)
IEEE DOI
1309
Pedestrian Detection
BibRef
Ouyang, W.L.[Wan-Li],
Zhou, H.[Hui],
Li, H.S.[Hong-Sheng],
Li, Q.Q.[Quan-Quan],
Yan, J.J.[Jun-Jie],
Wang, X.G.[Xiao-Gang],
Jointly Learning Deep Features, Deformable Parts, Occlusion and
Classification for Pedestrian Detection,
PAMI(40), No. 8, August 2018, pp. 1874-1887.
IEEE DOI
1807
Deformable models, Feature extraction, Image color analysis,
Image edge detection, Pattern analysis, Support vector machines,
object detection
BibRef
Tang, Y.[Yi],
Li, B.[Baopu],
Liu, M.[Min],
Chen, B.[Boyu],
Wang, Y.N.[Yao-Nan],
Ouyang, W.L.[Wan-Li],
AutoPedestrian: An Automatic Data Augmentation and Loss Function
Search Scheme for Pedestrian Detection,
IP(30), 2021, pp. 8483-8496.
IEEE DOI
2110
Search problems, Data models, Task analysis,
Optimization, Training, Object detection, Pedestrian detection,
loss function search
BibRef
Xu, D.,
Ouyang, W.L.[Wan-Li],
Ricci, E.,
Wang, X.G.[Xiao-Gang],
Sebe, N.,
Learning Cross-Modal Deep Representations for Robust Pedestrian
Detection,
CVPR17(4236-4244)
IEEE DOI
1711
Detectors, Feature extraction, Image reconstruction, Lighting,
Proposals, Robustness, Training
BibRef
Ouyang, W.L.[Wan-Li],
Li, H.,
Zeng, X.Y.[Xing-Yu],
Wang, X.G.[Xiao-Gang],
Learning Deep Representation with Large-Scale Attributes,
ICCV15(1895-1903)
IEEE DOI
1602
Computer vision
BibRef
Zhao, R.[Rui],
Ouyang, W.L.[Wan-Li],
Li, H.S.[Hong-Sheng],
Wang, X.G.[Xiao-Gang],
Saliency detection by multi-context deep learning,
CVPR15(1265-1274)
IEEE DOI
1510
BibRef
Wang, K.Z.[Ke-Ze],
Lin, L.[Liang],
Zuo, W.M.[Wang-Meng],
Gu, S.H.[Shu-Hang],
Zhang, L.[Lei],
Dictionary Pair Classifier Driven Convolutional Neural Networks for
Object Detection,
CVPR16(2138-2146)
IEEE DOI
1612
BibRef
Tian, Y.L.[Yong-Long],
Luo, P.[Ping],
Wang, X.G.[Xiao-Gang],
Tang, X.[Xiaoou],
Pedestrian Detection Aided by Deep Learning Semantic Tasks,
CVPR15(5079-5087)
IEEE DOI
1510
BibRef
Li, Y.K.[Yi-Kang],
Ouyang, W.L.[Wan-Li],
Wang, X.G.[Xiao-Gang],
Tang, X.[Xiao_Ou],
ViP-CNN: Visual Phrase Guided Convolutional Neural Network,
CVPR17(7244-7253)
IEEE DOI
1711
Feature extraction, Message passing, Object detection, Proposals,
Training, Visualization
BibRef
Kang, K.[Kai],
Li, H.S.[Hong-Sheng],
Yan, J.J.[Jun-Jie],
Zeng, X.Y.[Xing-Yu],
Yang, B.[Bin],
Xiao, T.[Tong],
Zhang, C.[Cong],
Wang, Z.[Zhe],
Wang, R.H.[Ruo-Hui],
Wang, X.G.[Xiao-Gang],
Ouyang, W.L.[Wan-Li],
T-CNN: Tubelets With Convolutional Neural Networks for Object
Detection from Videos,
CirSysVideo(28), No. 10, October 2018, pp. 2896-2907.
IEEE DOI
1811
BibRef
Earlier: A1, A11, A2, A10, Only:
Object Detection from Video Tubelets with Convolutional Neural
Networks,
CVPR16(817-825)
IEEE DOI
1612
Videos, Object detection, Proposals, Neural networks, Training,
Convolutional codes, Target tracking, Object detection.
Feature extraction, Visualization
BibRef
Kang, K.,
Li, H.S.[Hong-Sheng],
Xiao, T.,
Ouyang, W.L.[Wan-Li],
Yan, J.,
Liu, X.,
Wang, X.G.[Xiao-Gang],
Object Detection in Videos with Tubelet Proposal Networks,
CVPR17(889-897)
IEEE DOI
1711
Feature extraction, Visualization
BibRef
Ouyang, W.L.[Wan-Li],
Zeng, X.Y.[Xing-Yu],
Wang, X.G.[Xiao-Gang],
Qiu, S.[Shi],
Luo, P.[Ping],
Tian, Y.L.[Yong-Long],
Li, H.S.[Hong-Sheng],
Yang, S.[Shuo],
Wang, Z.[Zhe],
Li, H.Y.[Hong-Yang],
Wang, K.[Kun],
Yan, J.J.[Jun-Jie],
Loy, C.C.[Chen-Change],
Tang, X.[Xiaoou],
DeepID-Net:
Object Detection with Deformable Part Based Convolutional Neural Networks,
PAMI(39), No. 7, July 2017, pp. 1320-1334.
IEEE DOI
1706
BibRef
Earlier: A1, A3, A2, A4, A5, A6, A7, A8, A9, A13, A14, Only:
DeepID-Net:
Deformable Deep Convolutional Neural Networks for Object Detection,
CVPR15(2403-2412)
IEEE DOI
1510
Context modeling, Deformable models, Machine learning,
Neural networks, Object detection, Training, Visualization, CNN,
convolutional neural networks, deep learning, deep model, object detection.
BibRef
Yang, L.J.[Lin-Jie],
Liu, J.Z.[Jian-Zhuang],
Tang, X.[Xiaoou],
Object Detection and Viewpoint Estimation with Auto-masking Neural
Network,
ECCV14(III: 441-455).
Springer DOI
1408
BibRef
Ouyang, W.L.[Wan-Li],
Wang, K.[Kun],
Zhu, X.[Xin],
Wang, X.G.[Xiao-Gang],
Chained Cascade Network for Object Detection,
ICCV17(1956-1964)
IEEE DOI
1802
image classification, inference mechanisms,
learning (artificial intelligence), object detection, CC-Net,
Training
BibRef
Wang, Z.[Zhe],
Li, H.S.[Hong-Sheng],
Ouyang, W.L.[Wan-Li],
Wang, X.G.[Xiao-Gang],
Learnable Histogram:
Statistical Context Features for Deep Neural Networks,
ECCV16(I: 246-262).
Springer DOI
1611
BibRef
Ouyang, W.L.[Wan-Li],
Yang, X.,
Zhang, C.,
Yang, X.,
Factors in Finetuning Deep Model for Object Detection with Long-Tail
Distribution,
CVPR16(864-873)
IEEE DOI
1612
BibRef
Yang, W.,
Ouyang, W.L.[Wan-Li],
Li, H.S.[Hong-Sheng],
Wang, X.G.[Xiao-Gang],
End-to-End Learning of Deformable Mixture of Parts and Deep
Convolutional Neural Networks for Human Pose Estimation,
CVPR16(3073-3082)
IEEE DOI
1612
BibRef
Chu, X.[Xiao],
Ouyang, W.L.[Wan-Li],
Li, H.S.[Hong-Sheng],
Wang, X.G.[Xiao-Gang],
Structured Feature Learning for Pose Estimation,
CVPR16(4715-4723)
IEEE DOI
1612
BibRef
Earlier: A2, A1, A4, Only:
Multi-source Deep Learning for Human Pose Estimation,
CVPR14(2337-2344)
IEEE DOI
1409
Deep learning
BibRef
Zeng, X.Y.[Xing-Yu],
Ouyang, W.L.[Wan-Li],
Wang, M.[Meng],
Wang, X.G.[Xiao-Gang],
Deep Learning of Scene-Specific Classifier for Pedestrian Detection,
ECCV14(III: 472-487).
Springer DOI
1408
BibRef
Ouyang, W.L.[Wan-Li],
Zeng, X.Y.[Xing-Yu],
Wang, X.G.[Xiao-Gang],
Partial Occlusion Handling in Pedestrian Detection With a Deep Model,
CirSysVideo(26), No. 11, November 2016, pp. 2123-2137.
IEEE DOI
1609
BibRef
Earlier: A1, A3, Only:
A discriminative deep model for pedestrian detection with occlusion
handling,
CVPR12(3258-3265).
IEEE DOI
1208
Deformable models
BibRef
Tian, Y.L.[Yong-Long],
Luo, P.[Ping],
Wang, X.G.[Xiao-Gang],
Tang, X.[Xiaoou],
Deep Learning Strong Parts for Pedestrian Detection,
ICCV15(1904-1912)
IEEE DOI
1602
BibRef
Earlier: A2, A1, A3, A4:
Switchable Deep Network for Pedestrian Detection,
CVPR14(899-906)
IEEE DOI
1409
BibRef
Earlier: A2, A3, A4, Only:
Pedestrian Parsing via Deep Decompositional Network,
ICCV13(2648-2655)
IEEE DOI
1403
Detectors.
deep learning; pedestrian parsing
See also Deep Sum-Product Architecture for Robust Facial Attributes Analysis, A.
BibRef
Ouyang, W.L.[Wan-Li],
Wang, X.G.[Xiao-Gang],
Joint Deep Learning for Pedestrian Detection,
ICCV13(2056-2063)
IEEE DOI
1403
Pedestrian Detection
BibRef
Tomè, D.,
Monti, F.,
Baroffio, L.,
Bondi, L.,
Tagliasacchi, M.,
Tubaro, S.,
Deep Convolutional Neural Networks for pedestrian detection,
SP:IC(47), No. 1, 2016, pp. 482-489.
Elsevier DOI
1610
Deep learning
BibRef
Paisitkriangkrai, S.[Sakrapee],
Shen, C.H.[Chun-Hua],
van den Hengel, A.J.[Anton J.],
Pedestrian Detection with Spatially Pooled Features and Structured
Ensemble Learning,
PAMI(38), No. 6, June 2016, pp. 1243-1257.
IEEE DOI
1605
BibRef
Earlier:
Strengthening the Effectiveness of Pedestrian Detection with Spatially
Pooled Features,
ECCV14(IV: 546-561).
Springer DOI
1408
BibRef
Earlier:
Efficient Pedestrian Detection by Directly Optimizing the Partial
Area under the ROC Curve,
ICCV13(1057-1064)
IEEE DOI
1403
Boosting
BibRef
Ribeiro, D.[David],
Nascimento, J.C.[Jacinto C.],
Bernardino, A.[Alexandre],
Carneiro, G.[Gustavo],
Improving the performance of pedestrian detectors using convolutional
learning,
PR(61), No. 1, 2017, pp. 641-649.
Elsevier DOI
1705
BibRef
And: A1, A4, A2, A3:
Multi-channel Convolutional Neural Network Ensemble for Pedestrian
Detection,
IbPRIA17(122-130).
Springer DOI
1706
Pedestrian detection
BibRef
Jung, S.I.[Sang-Il],
Hong, K.S.[Ki-Sang],
Deep network aided by guiding network for pedestrian detection,
PRL(90), No. 1, 2017, pp. 43-49.
Elsevier DOI
1704
BibRef
And:
Direct multi-scale dual-stream network for pedestrian detection,
ICIP17(156-160)
IEEE DOI
1803
Semantics, Training, Pedestrian detection, deep convolutional neural network.
BibRef
Htike, K.K.[Kyaw Kyaw],
Efficient holistic feature basis learning for pedestrian detection,
IJCVR(8), No. 1, 2018, pp. 74-84.
DOI Link
1804
BibRef
Htike, K.K.[Kyaw Kyaw],
Hogg, D.C.[David C.],
Weakly supervised pedestrian detector training by unsupervised prior
learning and cue fusion in videos,
ICIP14(2338-2342)
IEEE DOI
1502
Computer vision
BibRef
Htike, K.K.[Kyaw Kyaw],
Hogg, D.C.[David C.],
Efficient Non-iterative Domain Adaptation of Pedestrian Detectors to
Video Scenes,
ICPR14(654-659)
IEEE DOI
1412
BibRef
Earlier:
Unsupervised Detector Adaptation by Joint Dataset Feature Learning,
ICCVG14(270-277).
Springer DOI
1410
Adapt pedestrian detector for broader use.
BibRef
Li, J.,
Liang, X.,
Shen, S.,
Xu, T.,
Feng, J.,
Yan, S.,
Scale-Aware Fast R-CNN for Pedestrian Detection,
MultMed(20), No. 4, April 2018, pp. 985-996.
IEEE DOI
1804
Detectors, Feature extraction, Logic gates, Proposals, Robustness,
Skeleton, Training, Pedestrian detection, deep learning, scale-aware
BibRef
Maggiani, L.[Luca],
Bourrasset, C.[Cédric],
Quinton, J.C.[Jean-Charles],
Berry, F.[François],
Sérot, J.[Jocelyn],
Bio-inspired heterogeneous architecture for real-time pedestrian
detection applications,
RealTimeIP(14), No. 3, March 2018, pp. 535-548.
Springer DOI
1804
BibRef
Park, K.[Kihong],
Kim, S.[Seungryong],
Sohn, K.H.[Kwang-Hoon],
Unified multi-spectral pedestrian detection based on probabilistic
fusion networks,
PR(80), 2018, pp. 143-155.
Elsevier DOI
1805
Multi-spectral sensor fusion, Pedestrian detection,
Channel weighting fusion, Probabilistic fusion
BibRef
Choi, S.,
Kim, S.[Seungryong],
Park, K.[Kihong],
Sohn, K.H.[Kwang-Hoon],
Multispectral human co-segmentation via joint convolutional neural
networks,
ICIP17(3115-3119)
IEEE DOI
1803
Color, Estimation, Feature extraction, Image color analysis,
Image segmentation, Integrated circuits, Task analysis,
weakly supervised learning
BibRef
Choi, H.I.[Hang-Il],
Kim, S.[Seungryong],
Park, K.[Kihong],
Sohn, K.H.[Kwang-Hoon],
Multi-spectral pedestrian detection based on accumulated object
proposal with fully convolutional networks,
ICPR16(621-626)
IEEE DOI
1705
Color, Feature extraction, Finite impulse response filters,
Image color analysis, Proposals, Robustness, Support, vector, machines
BibRef
Zhang, X.,
Cheng, L.,
Li, B.,
Hu, H.M.,
Too Far to See? Not Really!: Pedestrian Detection With Scale-Aware
Localization Policy,
IP(27), No. 8, August 2018, pp. 3703-3715.
IEEE DOI
1806
feature extraction, image classification, image representation,
learning (artificial intelligence), neural nets,
sequence of coordinate transformations
BibRef
Hu, Q.,
Wang, P.,
Shen, C.,
van den Hengel, A.,
Porikli, F.M.[Fatih M.],
Pushing the Limits of Deep CNNs for Pedestrian Detection,
CirSysVideo(28), No. 6, June 2018, pp. 1358-1368.
IEEE DOI
1806
Detectors, Feature extraction, Labeling, Object detection,
Proposals, Training, Convolutional feature map (CFM),
pedestrian detection
BibRef
Lahouli, I.[Ichraf],
Karakasis, E.[Evangelos],
Haelterman, R.[Robby],
Chtourou, Z.[Zied],
de Cubber, G.[Geert],
Gasteratos, A.[Antonios],
Attia, R.[Rabah],
Hot spot method for pedestrian detection using saliency maps, discrete
Chebyshev moments and support vector machine,
IET-IPR(12), No. 7, July 2018, pp. 1284-1291.
DOI Link
1806
BibRef
Wu, S.[Si],
Wong, H.S.[Hau-San],
Wang, S.F.[Shu-Feng],
Variant SemiBoost for Improving Human Detection in Application Scenes,
CirSysVideo(28), No. 7, July 2018, pp. 1595-1608.
IEEE DOI
1807
Adaptation models, Benchmark testing, Boosting, Detectors,
Feature extraction, Support vector machines, Training,
weighted similarity
BibRef
Sun, H.Y.[Hao-Yun],
Zhou, J.H.[Jie-Han],
Liu, Y.[Yan],
Gong, W.J.[Wen-Juan],
Deep learning-based real-time fine-grained pedestrian recognition using
stream processing,
IET-ITS(12), No. 7, September 2018, pp. 602-609.
DOI Link
1808
BibRef
Li, J.A.[Jian-An],
Liang, X.D.[Xiao-Dan],
Shen, S.M.[Sheng-Mei],
Xu, T.F.[Ting-Fa],
Feng, J.S.[Jia-Shi],
Yan, S.C.[Shui-Cheng],
Scale-Aware Fast R-CNN for Pedestrian Detection,
MultMed(20), No. 4, April 2018, pp. 985-996.
IEEE DOI
1804
Detectors, Feature extraction, Logic gates, Proposals, Robustness,
Skeleton, Training, Pedestrian detection, deep learning, scale-aware
BibRef
Li, J.A.[Jian-An],
Liang, X.D.[Xiao-Dan],
Wei, Y.C.[Yun-Chao],
Xu, T.F.[Ting-Fa],
Feng, J.S.[Jia-Shi],
Yan, S.C.[Shui-Cheng],
Perceptual Generative Adversarial Networks for Small Object Detection,
CVPR17(1951-1959)
IEEE DOI
1711
Feature extraction, Generators, Image resolution,
Object detection, Training
BibRef
Li, C.Y.[Cheng-Yang],
Song, D.[Dan],
Tong, R.F.[Ruo-Feng],
Tang, M.[Min],
Illumination-aware faster R-CNN for robust multispectral pedestrian
detection,
PR(85), 2019, pp. 161-171.
Elsevier DOI
1810
Multispectral pedestrian detection, Illumination-aware, Gated fusion
BibRef
Xiao, J.M.[Ji-Min],
Xie, Y.C.[Yan-Chun],
Tillo, T.[Tammam],
Huang, K.Z.[Kai-Zhu],
Wei, Y.C.[Yun-Chao],
Feng, J.S.[Jia-Shi],
IAN: The Individual Aggregation Network for Person Search,
PR(87), 2019, pp. 332-340.
Elsevier DOI
1812
person search, re-identification, pedestrian detection,
softmax loss, center loss, dropout
BibRef
Chen, Y.F.[Yun-Fan],
Xie, H.[Han],
Shin, H.[Hyunchul],
Multi-layer fusion techniques using a CNN for multispectral pedestrian
detection,
IET-CV(12), No. 8, December 2018, pp. 1179-1187.
DOI Link
1812
BibRef
Cao, Y.P.[Yan-Peng],
Guan, D.[Dayan],
Wu, Y.[Yulun],
Yang, J.X.[Jiang-Xin],
Cao, Y.L.[Yan-Long],
Yang, M.Y.[Michael Ying],
Box-level segmentation supervised deep neural networks for accurate
and real-time multispectral pedestrian detection,
PandRS(150), 2019, pp. 70-79.
Elsevier DOI
1903
Multispectral data, Pedestrian detection, Deep neural networks,
Box-level segmentation, Real-time application
BibRef
Li, Z.Q.[Zhao-Qing],
Chen, Z.[Zhenxue],
Wu, Q.M.J.[Q. M. Jonathan],
Liu, C.Y.[Cheng-Yun],
Real-time pedestrian detection with deep supervision in the wild,
SIViP(13), No. 4, June 2019, pp. 761-769.
Springer DOI
1906
BibRef
Shen, C.[Chao],
Zhao, X.M.[Xiang-Mo],
Fan, X.[Xing],
Lian, X.Y.[Xin-Yu],
Zhang, F.[Fan],
Kreidieh, A.R.[Abdul Rahman],
Liu, Z.W.[Zhan-Wen],
Multi-receptive field graph convolutional neural networks for
pedestrian detection,
IET-ITS(13), No. 9, September 2019, pp. 1319-1328.
DOI Link
1908
BibRef
Wu, S.,
Wu, W.,
Lei, S.,
Lin, S.,
Li, R.,
Yu, Z.,
Wong, H.,
Semi-Supervised Human Detection via Region Proposal Networks Aided by
Verification,
IP(29), 2020, pp. 1562-1574.
IEEE DOI
1911
Proposals, Training, Feature extraction, Task analysis, Data models,
Detectors, Benchmark testing, Human detection,
saliency detection
BibRef
Shojaei, G.[Ghazaleh],
Razzazi, F.[Farbod],
Semi-supervised domain adaptation for pedestrian detection in video
surveillance based on maximum independence assumption,
MultInfoRetr(8), No. 4, December 2019, pp. 241-252.
WWW Link.
1912
BibRef
Sun, R.[Rui],
Wang, H.H.[Hui-Hui],
Zhang, J.[Jun],
Zhang, X.D.[Xu-Dong],
Attention-Guided Region Proposal Network for Pedestrian Detection,
IEICE(E102-D), No. 10, October 2019, pp. 2072-2076.
WWW Link.
1912
BibRef
Qiu, J.,
Wang, L.,
Wang, Y.,
Hu, Y.H.,
Efficient Proposals: Scale Estimation for Object Proposals in
Pedestrian Detection Tasks,
SPLetters(27), 2020, pp. 855-859.
IEEE DOI
2006
Proposals, Training, Surveillance, Neural networks, Measurement,
Object detection, Cameras, Object detection, multilayer perceptron,
video surveillance system
BibRef
Ojala, R.,
Vepsäläinen, J.,
Hanhirova, J.,
Hirvisalo, V.,
Tammi, K.,
Novel Convolutional Neural Network-Based Roadside Unit for Accurate
Pedestrian Localisation,
ITS(21), No. 9, September 2020, pp. 3756-3765.
IEEE DOI
2008
Roads, Vehicles, Safety, Cameras, Object detection, Real-time systems,
Global Positioning System, Cameras,
vehicle safety
BibRef
Zou, T.T.[Teng-Tao],
Yang, S.M.[Shang-Ming],
Zhang, Y.[Yun],
Ye, M.[Mao],
Attention guided neural network models for occluded pedestrian
detection,
PRL(131), 2020, pp. 91-97.
Elsevier DOI
2004
Pedestrian detection, Occlusion, Convolutional neural networks,
Attention networks, Recurrent neural networks
BibRef
Qian, Y.,
Yang, M.,
Zhao, X.,
Wang, C.,
Wang, B.,
Oriented Spatial Transformer Network for Pedestrian Detection Using
Fish-Eye Camera,
MultMed(22), No. 2, February 2020, pp. 421-431.
IEEE DOI
2001
Cameras, Detectors, Feature extraction, Distortion, Deep learning,
Lenses, Training, Pedestrian detection, deep learning,
fish-eye image dataset
BibRef
Ding, L.[Lu],
Wang, Y.[Yong],
Laganière, R.[Robert],
Huang, D.[Dan],
Fu, S.[Shan],
Convolutional neural networks for multispectral pedestrian detection,
SP:IC(82), 2020, pp. 115764.
Elsevier DOI
2001
Multispectral pedestrian detection, R-FCN, Network-in-network
BibRef
Wei, X.[Xing],
Zhang, H.T.[Hai-Tao],
Liu, S.F.[Shao-Fan],
Lu, Y.[Yang],
Pedestrian detection in underground mines via parallel feature
transfer network,
PR(103), 2020, pp. 107195.
Elsevier DOI
2005
Pedestrian detection, Underground mine, Deep learning network,
Parallel feature transfer, Gated unit, Unmanned driving
BibRef
Kim, S.,
Gwak, I.,
Lee, S.,
Coarse-to-Fine Deep Learning of Continuous Pedestrian Orientation
Based on Spatial Co-Occurrence Feature,
ITS(21), No. 6, June 2020, pp. 2522-2533.
IEEE DOI
2006
Estimation, Visualization, Training, Task analysis,
Feature extraction, Deep learning, Complexity theory,
continuous orientation estimation
BibRef
Chen, C.[Chen],
Xiao, H.X.[Hua-Xin],
Liu, Y.[Yu],
Zhang, M.J.[Mao-Jun],
Dual-Task Integrated Network for Fast Pedestrian Detection in Crowded
Scenes,
IEICE(E103-D), No. 6, June 2020, pp. 1371-1379.
WWW Link.
2006
BibRef
Han, B.[Bing],
Wang, Y.H.[Yun-Hao],
Yang, Z.[Zheng],
Gao, X.B.[Xin-Bo],
Small-Scale Pedestrian Detection Based on Deep Neural Network,
ITS(21), No. 7, July 2020, pp. 3046-3055.
IEEE DOI
2007
Proposals, Feature extraction, Detectors, Vehicles, Convolution,
Entropy, Histograms, Pedestrian detection, deep learning,
VIP pedestrian dataset
BibRef
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PAMI(42), No. 9, September 2020, pp. 2195-2211.
IEEE DOI
2008
BibRef
Earlier:
Learning Complexity-Aware Cascades for Deep Pedestrian Detection,
ICCV15(3361-3369)
IEEE DOI
1602
Complexity theory, Detectors, Boosting, Feature extraction,
Proposals, Deep learning, Energy consumption,
complexity constrained learning.
Algorithm design and analysis
BibRef
Lee, Y.[Yongwoo],
Hwang, H.Y.[Hyek-Young],
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CirSysVideo(30), No. 8, August 2020, pp. 2663-2673.
IEEE DOI
2008
Feature extraction, Image segmentation, Semantics, Detectors,
Task analysis, Object detection, Neural networks, Human detection,
instance segmentation
BibRef
Zhao, Y.,
Yuan, Z.,
Chen, B.,
Training Cascade Compact CNN With Region-IoU for Accurate Pedestrian
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ITS(21), No. 9, September 2020, pp. 3777-3787.
IEEE DOI
2008
Detectors, Training, Proposals, Measurement, Feature extraction,
Pipelines, Object detection, Region-IoU, cascade compact CNN,
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BibRef
Wang, W.H.[Wen-Hao],
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Elsevier DOI
2011
Pedestrian detection, Panoramic vision,
Switchable normalization, Convolutional neural networks, Deep learning
BibRef
Wang, H.,
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Fast Pedestrian Detection With Attention-Enhanced Multi-Scale RPN and
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ITS(21), No. 12, December 2020, pp. 5086-5093.
IEEE DOI
2012
Feature extraction, Convolution, Proposals, Decision trees,
Detectors, Neural networks, Intelligent transportation systems, DNN
BibRef
Sheng, B.,
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Yang, W.,
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Discriminative Multi-View Subspace Feature Learning for Action
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CirSysVideo(30), No. 12, December 2020, pp. 4591-4600.
IEEE DOI
2012
Feature extraction, Visualization, Testing, Training,
Computational modeling, Task analysis, Data mining,
multi-level feature fusion
BibRef
Yang, C.H.Y.[Chen-Hong-Yi],
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Wang, K.H.[Kai-Hong],
Feng, Q.[Qi],
Betke, M.[Margrit],
Learning to Separate: Detecting Heavily-Occluded Objects in Urban
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ECCV20(XVIII:530-546).
Springer DOI
2012
intra-class occlusions.
Cars and pedestrians.
BibRef
Hsu, W.Y.[Wei-Yen],
Lin, W.Y.[Wen-Yen],
Ratio-and-Scale-Aware YOLO for Pedestrian Detection,
IP(30), 2021, pp. 934-947.
IEEE DOI
2012
Image resolution, Deep learning, Proposals, Object detection,
Feature extraction, Detection algorithms,
scale-aware
BibRef
Dimiccoli, M.[Mariella],
Wendt, H.[Herwig],
Learning Event Representations for Temporal Segmentation of Image
Sequences by Dynamic Graph Embedding,
IP(30), 2021, pp. 1476-1486.
IEEE DOI
2101
Image segmentation, Image sequences, Motion segmentation, Videos,
Semantics, Training, Benchmark testing, Clustering,
temporal segmentation
BibRef
Jiao, Y.F.[Yi-Fan],
Yao, H.T.[Han-Tao],
Xu, C.S.[Chang-Sheng],
SAN: Selective Alignment Network for Cross-Domain Pedestrian
Detection,
IP(30), 2021, pp. 2155-2167.
IEEE DOI
2102
convolutional neural nets, feature extraction, image annotation,
image classification, iterative methods, pedestrian detection
BibRef
Jiao, Y.,
Yao, H.,
Xu, C.,
PEN: Pose-Embedding Network for Pedestrian Detection,
CirSysVideo(31), No. 3, March 2021, pp. 1150-1162.
IEEE DOI
2103
Visualization, Proposals, Detectors, Feature extraction,
Object detection, Pose estimation, Fuses, Pedestrian detection,
pose information
BibRef
Xie, J.,
Pang, Y.,
Khan, M.H.,
Anwer, R.M.,
Khan, F.S.,
Shao, L.,
Mask-Guided Attention Network and Occlusion-Sensitive Hard Example
Mining for Occluded Pedestrian Detection,
IP(30), 2021, pp. 3872-3884.
IEEE DOI
2104
BibRef
Earlier: A2, A1, A3, A4, A5, A6:
Mask-Guided Attention Network for Occluded Pedestrian Detection,
ICCV19(4966-4974)
IEEE DOI
2004
Code, Pedestrian Detection.
WWW Link. Detectors, Standards, Feature extraction, Proposals,
Benchmark testing, Task analysis,
hard example mining.
convolutional neural nets, image annotation,
image classification, image segmentation, pedestrians,
Computer architecture
BibRef
Jin, Y.[Yi],
Zhang, Y.[Yue],
Cen, Y.G.[Yi-Gang],
Li, Y.D.[Yi-Dong],
Mladenovic, V.[Vladimir],
Voronin, V.[Viacheslav],
Pedestrian detection with super-resolution reconstruction for
low-quality image,
PR(115), 2021, pp. 107846.
Elsevier DOI
2104
Pedestrian detection, Low-quality, SRGAN, Faster R-CNN
BibRef
Yu, W.Y.[Wing-Yin],
Po, L.M.[Lai-Man],
Zhao, Y.Z.[Yu-Zhi],
Zhang, Y.J.[Yu-Jia],
Lau, K.W.[Kin-Wai],
FEANet: Foreground-edge-aware network with DenseASPOC for human
parsing,
IVC(109), 2021, pp. 104145.
Elsevier DOI
2105
Human parsing, Semantic segmentation,
Foreground-edge awareness, Non-local operation
BibRef
Zhang, S.S.[Shan-Shan],
Chen, D.[Di],
Yang, J.[Jian],
Schiele, B.[Bernt],
Guided Attention in CNNs for Occluded Pedestrian Detection and
Re-identification,
IJCV(129), No. 6, June 2021, pp. 1875-1892.
Springer DOI
2106
BibRef
Earlier: A1, A3, A4, Only:
Occluded Pedestrian Detection Through Guided Attention in CNNs,
CVPR18(6995-7003)
IEEE DOI
1812
Detectors, Feature extraction,
Object detection, Training, Body regions, Correlation
BibRef
Guo, Z.X.[Zhi-Xin],
Liao, W.Z.[Wen-Zhi],
Xiao, Y.F.[Yi-Fan],
Veelaert, P.[Peter],
Philips, W.[Wilfried],
Weak segmentation supervised deep neural networks for pedestrian
detection,
PR(119), 2021, pp. 108063.
Elsevier DOI
2106
Pedestrian detection, Semantic segmentation, Deep learning
BibRef
Xiao, Y.Q.[Yan-Qiu],
Zhou, K.[Kun],
Cui, G.Z.[Guang-Zhen],
Jia, L.H.[Lian-Hui],
Fang, Z.P.[Zhan-Peng],
Yang, X.C.[Xian-Chao],
Xia, Q.P.[Qiong-Pei],
Deep learning for occluded and multi-scale pedestrian detection:
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IET-IPR(15), No. 2, 2021, pp. 286-301.
DOI Link
2106
Survey, Pedestrian Detection.
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Zhao, Y.[Yang],
Yu, X.H.[Xiao-Han],
Gao, Y.S.[Yong-Sheng],
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Learning discriminative region representation for person retrieval,
PR(121), 2022, pp. 108229.
Elsevier DOI
2109
Person retrieval, Region representation
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Xu, Z.W.[Zhe-Wei],
Vong, C.M.[Chi-Man],
Wong, C.C.[Chi-Chong],
Liu, Q.[Qiong],
Ground Plane Context Aggregation Network for Day-and-Night on
Vehicular Pedestrian Detection,
ITS(22), No. 10, October 2021, pp. 6395-6406.
IEEE DOI
2110
Feature extraction, Semantics, Detectors, Proposals, Convolution,
Cameras, Pedestrian detection, ADAS
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Yao, Z.G.[Zong-Gui],
Yu, J.[Jun],
Ding, J.J.[Jia-Jun],
Contrastive learning of graph encoder for accelerating pedestrian
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IET-IPR(15), No. 14, 2021, pp. 3645-3660.
DOI Link
2112
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Qian, Y.Q.[Ye-Qiang],
Yang, M.[Ming],
Li, H.[Hao],
Wang, C.X.[Chun-Xiang],
Wang, B.[Bing],
Adversarial Training-Based Hard Example Mining for Pedestrian
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ITS(22), No. 12, December 2021, pp. 7688-7698.
IEEE DOI
2112
Nonlinear distortion, Cameras, Detectors, Training, Standards,
Intelligent transportation systems, Hard example mining,
fish-eye image
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Shah, V.[Vedant],
Agarwal, A.[Anmol],
Verlekar, T.T.[Tanmay Tulsidas],
Singh, R.[Raghavendra],
Adapting Deep Neural Networks for Pedestrian-Detection to Low-Light
Conditions without Re-training,
TradiCV21(2535-2541)
IEEE DOI
2112
Deep learning, Training, Adaptation models, Image segmentation,
Computational modeling, Surveillance, Pipelines
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Liang, Z.Y.[Zhi-Yuan],
Guo, K.[Kan],
Li, X.B.[Xiao-Bo],
Jin, X.G.[Xiao-Gang],
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Person Foreground Segmentation by Learning Multi-Domain Networks,
IP(31), 2022, pp. 585-597.
IEEE DOI
2112
Image segmentation, Feature extraction, Real-time systems,
Task analysis, Semantics, Faces, Training,
multi-domain learning
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Fradi, H.[Hajer],
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Unsupervised thermal-to-visible domain adaptation method for
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Elsevier DOI
2201
BibRef
Earlier:
Thermal Image Enhancement using Generative Adversarial Network for
Pedestrian Detection,
ICPR21(6509-6516)
IEEE DOI
2105
Pedestrian detection, Thermal imagery, Domain adaptation,
Domain classifier, Cross entropy loss, Focal loss, Faster R-CNN.
Night vision, Visualization, Superresolution, Noise reduction,
Robot vision systems, Detectors, Generative adversarial networks
BibRef
Liu, T.S.[Tian-Shan],
Lam, K.M.[Kin-Man],
Zhao, R.[Rui],
Qiu, G.P.[Guo-Ping],
Deep Cross-Modal Representation Learning and Distillation for
Illumination-Invariant Pedestrian Detection,
CirSysVideo(32), No. 1, January 2022, pp. 315-329.
IEEE DOI
2201
Feature extraction, Detectors, Task analysis, Lighting, Training,
Image segmentation, Semantics,
cross-modal representation
BibRef
Zhou, C.J.[Cheng-Ju],
Wu, M.Q.[Mei-Qing],
Lam, S.K.[Siew-Kei],
A Unified Multi-Task Learning Architecture for Fast and Accurate
Pedestrian Detection,
ITS(23), No. 2, February 2022, pp. 982-996.
IEEE DOI
2202
Semantics, Task analysis,
Computational complexity, Robustness, Feature extraction,
feature aggregation
BibRef
Lin, S.[Sihao],
Wu, W.H.[Wen-Hao],
Wu, S.[Si],
Xu, Y.[Yong],
Wong, H.S.[Hau-San],
Unreliable-to-Reliable Instance Translation for Semi-Supervised
Pedestrian Detection,
MultMed(24), 2022, pp. 728-739.
IEEE DOI
2202
Data models, Training, Reliability, Detectors, Task analysis,
Semantics, Visualization, Generative adversarial network,
semi-supervised learning
BibRef
Li, G.Z.[Gao-Zhe],
Wu, S.[Si],
Scene-Adaptive Instance Modification for Semisupervised Pedestrian
Detection,
MultMedMag(29), No. 4, October 2022, pp. 69-79.
IEEE DOI
2301
Detectors, Codes, Training data, Generative adversarial networks,
Annotations, Adaptation models, Image analysis
BibRef
Wu, S.[Si],
Lin, S.[Sihao],
Wu, W.H.[Wen-Hao],
Azzam, M.[Mohamed],
Wong, H.S.[Hau San],
Semi-Supervised Pedestrian Instance Synthesis and Detection With
Mutual Reinforcement,
ICCV19(5056-5065)
IEEE DOI
2004
game theory, image classification,
learning (artificial intelligence), minimax techniques,
Games
BibRef
Kim, J.U.[Jung Uk],
Park, S.[Sungjune],
Ro, Y.M.[Yong Man],
Uncertainty-Guided Cross-Modal Learning for Robust Multispectral
Pedestrian Detection,
CirSysVideo(32), No. 3, March 2022, pp. 1510-1523.
IEEE DOI
2203
Uncertainty, Reliability, Image color analysis, Feature extraction,
Task analysis, Lighting, Color, Multispectral pedestrian detection,
cross-modal learning
BibRef
Park, S.[Sungjune],
Kim, H.[Hyunjun],
Ro, Y.M.[Yong Man],
Robust pedestrian detection via constructing versatile pedestrian
knowledge bank,
PR(153), 2024, pp. 110539.
Elsevier DOI
2405
Versatile pedestrian knowledge bank, Pedestrian detection
BibRef
Park, S.[Sungjune],
Kim, J.U.[Jung Uk],
Song, J.M.[Jin Mo],
Ro, Y.M.[Yong Man],
Robust Multispectral Pedestrian Detection Via Spectral Position-Free
Feature Mapping,
ICIP23(1795-1799)
IEEE DOI
2312
BibRef
Park, S.[Sungjune],
Kim, J.U.[Jung Uk],
Kim, Y.G.[Yeon Gyun],
Moon, S.K.[Sang-Keun],
Ro, Y.M.[Yong Man],
Robust Multispectral Pedestrian Detection via Uncertainty-aware
Cross-modal Learning,
MMMod21(I:391-402).
Springer DOI
2106
BibRef
Wang, Y.[Yu],
Cao, C.[Cong],
Kato, J.[Jien],
Discriminative Part CNN for Pedestrian Detection,
IEICE(E105-D), No. 3, March 2022, pp. 700-712.
WWW Link.
2203
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Ryu, J.W.[Junh-Wan],
Kim, J.C.[Jong-Chan],
Kim, H.[Heegon],
Kim, S.[Sungho],
Multispectral interaction convolutional neural network for pedestrian
detection,
CVIU(223), 2022, pp. 103554.
Elsevier DOI
2210
Multispectral network, Multispectral fusion,
Multispectral interaction, Multispectral pedestrian detection
BibRef
Islam, M.M.[Muhammad Mobaidul],
Newaz, A.A.[Abdullah Al_Redwan],
Karimoddini, A.[Ali],
Pedestrian Detection for Autonomous Cars:
Inference Fusion of Deep Neural Networks,
ITS(23), No. 12, December 2022, pp. 23358-23368.
IEEE DOI
2212
Semantics, Feature extraction, Object detection, Detectors, Runtime,
Deep learning, Location awareness, Pedestrian detection,
deep learning
BibRef
Liu, W.[Wei],
Hasan, I.[Irtiza],
Liao, S.C.[Sheng-Cai],
Center and Scale Prediction: Anchor-free Approach for Pedestrian and
Face Detection,
PR(135), 2023, pp. 109071.
Elsevier DOI
2212
Object Detection, Convolutional Neural Networks,
Feature Detection, anchor-free, Anchor-free
BibRef
Khan, A.H.[Abdul Hannan],
Munir, M.[Mohsin],
van Elst, L.[Ludger],
Dengel, A.[Andreas],
F2DNet: Fast Focal Detection Network for Pedestrian Detection,
ICPR22(4658-4664)
IEEE DOI
2212
Computational modeling, Urban areas, Redundancy, Training data,
Detectors, Object detection,
BibRef
Kolluri, J.[Johnson],
Das, R.[Ranjita],
Intelligent multimodal pedestrian detection using hybrid
metaheuristic optimization with deep learning model,
IVC(131), 2023, pp. 104628.
Elsevier DOI
2303
Pedestrian detection, Metaheuristics, Deep learning, YOLO-v5,
Hybrid algorithms, Machine learning
BibRef
Lin, Z.B.[Ze-Bin],
Pei, W.J.[Wen-Jie],
Chen, F.L.[Fang-Lin],
Zhang, D.[David],
Lu, G.M.[Guang-Ming],
Pedestrian Detection by Exemplar-Guided Contrastive Learning,
IP(32), 2023, pp. 2003-2016.
IEEE DOI
2304
Feature extraction, Proposals, Semantics, Dictionaries, Detectors,
Adaptation models, Object detection, Pedestrian detection, contrastive learning
BibRef
Ma, C.J.[Chun-Jie],
Zhuo, L.[Li],
Li, J.F.[Jia-Feng],
Zhang, Y.T.[Yu-Tong],
Zhang, J.[Jing],
Cascade Transformer Decoder Based Occluded Pedestrian Detection With
Dynamic Deformable Convolution and Gaussian Projection Channel
Attention Mechanism,
MultMed(25), 2023, pp. 1529-1537.
IEEE DOI
2305
Transformers, Convolution, Feature extraction, Kernel, Decoding,
Object detection, Task analysis, Cascade transformer decoder,
occluded pedestrian detection
BibRef
Chen, Z.[Zhe],
Zhang, J.[Jing],
Xu, Y.F.[Yu-Fei],
Tao, D.C.[Da-Cheng],
Transformer-Based Context Condensation for Boosting Feature Pyramids in
Object Detection,
IJCV(131), No. 10, October 2023, pp. 2738-2756.
Springer DOI
2309
BibRef
Earlier: A1, A2, A4, Only:
Recurrent Glimpse-based Decoder for Detection with Transformer,
CVPR22(5250-5259)
IEEE DOI
2210
WWW Link. Training, Visualization, Pipelines, Detectors, Feature extraction,
Transformers, Recognition: detection, categorization, retrieval
BibRef
Symeonidis, C.[Charalampos],
Mademlis, I.[Ioannis],
Pitas, I.[Ioannis],
Nikolaidis, N.[Nikos],
Neural Attention-Driven Non-Maximum Suppression for Person Detection,
IP(32), 2023, pp. 2454-2467.
IEEE DOI
2305
Detectors, Object detection, Visualization, Task analysis, Training,
Proposals, Object recognition, Non-maximum suppression, deep neural networks
BibRef
Jeevarajan, M.K.,
Nirmal-Kumar, P.,
Reconfigurable Pedestrian Detection System Using Deep Learning for
Video Surveillance,
IEICE(E106-D), No. 9, September 2023, pp. 1610-1614.
WWW Link.
2310
BibRef
Li, J.[Jun],
Bi, Y.[Yuquan],
Wang, S.[Sumei],
Li, Q.M.[Qi-Ming],
CFRLA-Net: A Context-Aware Feature Representation Learning
Anchor-Free Network for Pedestrian Detection,
CirSysVideo(33), No. 9, September 2023, pp. 4948-4961.
IEEE DOI
2310
BibRef
Ni, H.[Han],
Wang, W.[Wenna],
Yun, S.[Shuai],
Zhao, Z.X.[Zi-Xu],
Zhang, X.W.[Xiu-Wei],
Modality-Independent Regression and Training for Improving
Multispectral Pedestrian Detection,
ICIVC22(75-80)
IEEE DOI
2301
Training, Annotations, Fuses, Detectors, Thermal sensors,
Adversarial machine learning, multi-modal annotation
BibRef
Pavlitskaya, S.[Svetlana],
Yikmis, S.[Siyar],
Zöllner, J.M.[J. Marius],
Is Neuron Coverage Needed to Make Person Detection More Robust?,
FaDE-TCV22(2888-2896)
IEEE DOI
2210
Measurement, Deep learning, Computer bugs, Neurons, Neural networks,
Training data
BibRef
Kim, J.U.[Jung Uk],
Park, S.[Sungjune],
Ro, Y.M.[Yong Man],
Robust Small-scale Pedestrian Detection with Cued Recall via Memory
Learning,
ICCV21(3030-3039)
IEEE DOI
2203
Visualization, Object detection,
Detection and localization in 2D and 3D,
BibRef
Zhang, H.[Heng],
Fromont, E.[Elisa],
Lefevre, S.[Sébastien],
Avignon, B.[Bruno],
Guided Attentive Feature Fusion for Multispectral Pedestrian
Detection,
WACV21(72-80)
IEEE DOI
2106
Deep learning, Visualization, Fuses,
Object detection, Computer architecture
BibRef
Ding, M.Y.[Meng-Yuan],
Zhang, S.S.[Shan-Shan],
Yang, J.[Jian],
Learning a Dynamic High-Resolution Network for Multi-Scale Pedestrian
Detection,
ICPR21(9076-9082)
IEEE DOI
2105
Adaptive systems, Object detection,
Logic gates, Benchmark testing, Pattern recognition
BibRef
Cheng, Q.[Qi],
Chen, M.Q.[Ming-Qin],
Wu, Y.J.[Ying-Jie],
Chen, F.[Fei],
Lin, S.P.[Shi-Ping],
MagnifierNet: Learning Efficient Small-scale Pedestrian Detector
towards Multiple Dense Regions,
ICPR21(1483-1490)
IEEE DOI
2105
Convolution, Detectors, Benchmark testing, Feature extraction,
Pattern recognition, Classification algorithms
BibRef
Isler, V.,
Lee, D.D.,
On-Device Event Filtering with Binary Neural Networks for Pedestrian
Detection Using Neuromorphic Vision Sensors,
ICIP20(3084-3088)
IEEE DOI
2011
Voltage control, Neural networks, Hardware,
Detectors, Cameras, dynamic vision sensors, binary neural networks,
embedded systems
BibRef
Zhou, C.,
Yang, M.,
Yuan, J.,
Discriminative Feature Transformation for Occluded Pedestrian
Detection,
ICCV19(9556-9565)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image classification, object detection, pedestrians,
Task analysis
BibRef
Bertoni, L.,
Kreiss, S.,
Alahi, A.,
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty
Estimation,
ICCV19(6860-6870)
IEEE DOI
2004
feedforward neural nets, learning (artificial intelligence),
pedestrians, pose estimation, statistical distributions, Machine learning
BibRef
Brazil, G.[Garrick],
Liu, X.M.[Xiao-Ming],
Pedestrian Detection With Autoregressive Network Phases,
CVPR19(7224-7233).
IEEE DOI
2002
BibRef
Yan, Y.C.[Yi-Chao],
Zhang, Q.A.[Qi-Ang],
Ni, B.B.[Bing-Bing],
Zhang, W.D.[Wen-Dong],
Xu, M.H.[Ming-Hao],
Yang, X.K.[Xiao-Kang],
Learning Context Graph for Person Search,
CVPR19(2153-2162).
IEEE DOI
2002
BibRef
Favorskaya, M.N.,
Andreev, V.V.,
The Study of Activation Functions in Deep Learning for Pedestrian
Detection and Tracking,
PTVSBB19(53-59).
DOI Link
1912
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Xi, W.,
Chen, J.,
Lin, Q.,
Allebach, J.P.,
High-Accuracy Automatic Person Segmentation with Novel Spatial
Saliency Map,
ICIP19(1560-1564)
IEEE DOI
1910
Person Segmentation, Lightweight CNN
BibRef
Yin, R.,
Multi-Resolution Generative Adversarial Networks for Tiny-Scale
Pedestrian Detection,
ICIP19(1665-1669)
IEEE DOI
1910
Pedestrian detection, Super-resolution, Generative adversarial network
BibRef
Chen, R.,
Ai, H.,
Shang, C.,
Chen, L.,
Zhuang, Z.,
Learning Lightweight Pedestrian Detector with Hierarchical Knowledge
Distillation,
ICIP19(1645-1649)
IEEE DOI
1910
Pedestrian detection, knowledge distillation, model compression
BibRef
Kim, M.,
Joung, S.,
Park, K.,
Kim, S.,
Sohn, K.,
Unpaired Cross-Spectral Pedestrian Detection Via Adversarial Feature
Learning,
ICIP19(1650-1654)
IEEE DOI
1910
Cross-spectral pedestrian detection, adversarial learning, common feature space
BibRef
Amato, G.[Giuseppe],
Ciampi, L.[Luca],
Falchi, F.[Fabrizio],
Gennaro, C.[Claudio],
Messina, N.[Nicola],
Learning Pedestrian Detection from Virtual Worlds,
CIAP19(I:302-312).
Springer DOI
1909
BibRef
Tyler-Rodrigue, M.,
Green, R.,
Track Cyclist Detection and Identification using Mask R-CNN and
K-means Clustering,
IVCNZ19(1-6)
IEEE DOI
2004
cameras, computerised instrumentation, convolutional neural nets,
image colour analysis, image segmentation, image sensors, identification
BibRef
Zhang, L.,
Zhu, X.,
Chen, X.,
Yang, X.,
Lei, Z.,
Liu, Z.,
Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian
Detection,
ICCV19(5126-5136)
IEEE DOI
2004
Code, Convolutional Neural Networks.
WWW Link. convolutional neural nets, feature extraction,
image colour analysis, image fusion, infrared imaging, Color
BibRef
Weng, X.,
Wu, S.,
Beainy, F.,
Kitani, K.M.,
Rotational Rectification Network:
Enabling Pedestrian Detection for Mobile Vision,
WACV18(1084-1092)
IEEE DOI
1806
cameras, convolution, estimation theory,
feature extraction, feedforward neural nets, mobile computing,
Robustness
BibRef
Kruthiventi, S.S.S.,
Sahay, P.,
Biswal, R.,
Low-light pedestrian detection from RGB images using multi-modal
knowledge distillation,
ICIP17(4207-4211)
IEEE DOI
1803
Data mining, Feature extraction, Knowledge engineering, Lighting,
Proposals, Training, Visualization, Convolutional Neural Networks,
Low-light pedestrian detection
BibRef
Gajjar, V.,
Khandhediya, Y.,
Gumani, A.,
Mavani, V.,
Raval, M.S.,
ViS-HuD: Using Visual Saliency to Improve Human Detection with
Convolutional Neural Networks,
Cognitive18(1989-19898)
IEEE DOI
1812
Visualization, Feature extraction, Training, Saliency detection,
Computational modeling, Benchmark testing
BibRef
Li, X.,
Liu, Y.,
Chen, Z.,
Zhou, J.,
Wu, Y.,
Fused Discriminative Metric Learning for Low Resolution Pedestrian
Detection,
ICIP18(958-962)
IEEE DOI
1809
Training, Extraterrestrial measurements, Feature extraction,
Euclidean distance, Visualization, Learning systems,
Metric learning
BibRef
Wang, H.,
Xu, Y.,
Ni, B.,
Zhuang, L.,
Xu, H.,
Flexible Network Binarization with Layer-Wise Priority,
ICIP18(2346-2350)
IEEE DOI
1809
Neural networks, Training, Task analysis, Convolution, Optimization,
Computational modeling, Image coding,
pedestrian detection
BibRef
Vandersteegen, M.[Maarten],
van Beeck, K.[Kristof],
Goedemé, T.[Toon],
Real-Time Multispectral Pedestrian Detection with a Single-Pass Deep
Neural Network,
ICIAR18(419-426).
Springer DOI
1807
BibRef
Tahboub, K.,
Güera, D.,
Reibman, A.R.,
Delp, E.J.,
Quality-adaptive deep learning for pedestrian detection,
ICIP17(4187-4191)
IEEE DOI
1803
Degradation, Detectors, Estimation, Image coding, Streaming media,
Training, Video sequences, Pedestrian detection,
intelligent video surveillance
BibRef
Tahboub, K.,
Reibman, A.R.,
Delp, E.J.,
Accuracy prediction for pedestrian detection,
ICIP17(4192-4196)
IEEE DOI
1803
Degradation, Detectors, Histograms, Predictive models,
Quality assessment, Streaming media, Video recording,
video quality
BibRef
Ghosh, S.,
Amon, P.,
Hutter, A.,
Kaup, A.,
Reliable pedestrian detection using a deep neural network trained on
pedestrian counts,
ICIP17(685-689)
IEEE DOI
1803
Feature extraction, Machine learning, Merging, Neural networks,
Task analysis, Training, Training data, CNN, Counting Model,
Pedestrian Detection
BibRef
Sun, Y.,
Zheng, L.,
Deng, W.,
Wang, S.,
SVDNet for Pedestrian Retrieval,
ICCV17(3820-3828)
IEEE DOI
1802
convolution, image recognition, image representation,
iterative methods, learning (artificial intelligence),
Training
BibRef
Liu, X.,
Zhao, H.,
Tian, M.,
Sheng, L.,
Shao, J.,
Yi, S.,
Yan, J.,
Wang, X.,
HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis,
ICCV17(350-359)
IEEE DOI
1802
feature extraction, image representation,
learning (artificial intelligence), neural nets,
Visualization
BibRef
Huang, X.Y.[Xin-Yu],
Xu, J.L.[Jiao-Long],
Guo, G.[Gang],
Zheng, E.[Ergong],
Hybrid Distance Metric Learning for Real-Time Pedestrian Detection and
Re-identification,
CVS17(448-458).
Springer DOI
1711
BibRef
Huang, S.,
Ramanan, D.,
Expecting the Unexpected:
Training Detectors for Unusual Pedestrians with Adversarial Imposters,
CVPR17(4664-4673)
IEEE DOI
1711
Detectors, Engines, Pipelines, Rendering (computer graphics),
Solid modeling, Training
BibRef
Zhao, J.[Jian],
Li, J.S.[Jian-Shu],
Nie, X.C.[Xue-Cheng],
Zhao, F.[Fang],
Chen, Y.P.[Yun-Peng],
Wang, Z.C.[Zhe-Can],
Feng, J.S.[Jia-Shi],
Yan, S.C.[Shui-Cheng],
Self-Supervised Neural Aggregation Networks for Human Parsing,
Crowd17(1595-1603)
IEEE DOI
1709
Aggregates, Benchmark testing,
Neural networks, Semantics, Training
BibRef
Verbickas, R.,
Laganiere, R.,
Laroche, D.,
Zhu, C.,
Xu, X.,
Ors, A.,
SqueezeMap: Fast Pedestrian Detection on a Low-Power Automotive
Processor Using Efficient Convolutional Neural Networks,
ECVW17(463-471)
IEEE DOI
1709
Autonomous vehicles, Cameras, Computational efficiency,
Computational modeling, Fires, Heating, systems
BibRef
Zhu, Y.S.[You-Song],
Wang, J.Q.[Jin-Qiao],
Zhao, C.Y.[Chao-Yang],
Guo, H.Y.[Hai-Yun],
Lu, H.Q.[Han-Qing],
Scale-Adaptive Deconvolutional Regression Network for Pedestrian
Detection,
ACCV16(II: 416-430).
Springer DOI
1704
BibRef
Cheng, Z.Y.[Zhi-Yi],
Li, X.X.[Xiao-Xiao],
Loy, C.C.[Chen Change],
Pedestrian Color Naming via Convolutional Neural Network,
ACCV16(II: 35-51).
Springer DOI
1704
BibRef
Bowers, J.,
Green, R.,
Improving pedestrian detection,
ICVNZ16(1-5)
IEEE DOI
1701
Biological neural networks
BibRef
San-Biagio, M.[Marco],
Ulas, A.[Aydin],
Crocco, M.[Marco],
Cristani, M.[Marco],
Castellani, U.[Umberto],
Murino, V.[Vittorio],
A Multiple Kernel Learning Approach to Multi-Modal Pedestrian
Classification,
ICPR12(2412-2415).
WWW Link.
1302
BibRef
Dong, P.,
Wang, W.,
Fan, M.,
Wang, R.,
Li, G.,
Mask-streaming CNN for pedestrian detection,
VCIP17(1-4)
IEEE DOI
1804
feature extraction, image classification, image representation,
learning (artificial intelligence), neural nets,
Semantic Characters
BibRef
Cavazza, J.[Jacopo],
Morerio, P.[Pietro],
Murino, V.[Vittorio],
A Compact Kernel Approximation for 3D Action Recognition,
CIAP17(I:211-222).
Springer DOI
1711
BibRef
Earlier:
When Kernel Methods Meet Feature Learning:
Log-Covariance Network for Action Recognition From Skeletal Data,
ActionCh17(1251-1258)
IEEE DOI
1709
Covariance matrices,
Kernel, Neural networks, Training
BibRef
Cavazza, J.[Jacopo],
Zunino, A.[Andrea],
San-Biagio, M.[Marco],
Murino, V.[Vittorio],
Kernelized covariance for action recognition,
ICPR16(408-413)
IEEE DOI
1705
Activity recognition, Covariance matrices, Data models,
Hidden Markov models, Kernel,
BibRef
Sangineto, E.[Enver],
Cristani, M.[Marco],
del Bue, A.[Alessio],
Murino, V.[Vittorio],
Learning Discriminative Spatial Relations for Detector Dictionaries:
An Application to Pedestrian Detection,
ECCV12(II: 273-286).
Springer DOI
1210
BibRef
Wu, C.H.[Chi-Hao],
Gan, W.H.[Wei-Hao],
Lan, D.[De],
Kuo, C.C.J.[C.C. Jay],
Boosted Convolutional Neural Networks (BCNN) for Pedestrian Detection,
WACV17(540-549)
IEEE DOI
1609
Boosting, Detectors, Feature extraction,
Neural networks, Performance gain, Training
BibRef
Du, X.Z.[Xian-Zhi],
El-Khamy, M.[Mostafa],
Lee, J.W.[Jung-Won],
Davis, L.S.[Larry S.],
Fused DNN: A Deep Neural Network Fusion Approach to Fast and Robust
Pedestrian Detection,
WACV17(953-961)
IEEE DOI
1609
Context, Detectors, Fuses, Generators,
Neural networks, Semantics
BibRef
Yamada, K.,
Pedestrian detection with a resolution-aware convolutional network,
ICPR16(591-596)
IEEE DOI
1705
Cameras, Detectors, Feature extraction,
Image resolution, Training
BibRef
Dong, P.L.[Pei-Lei],
Wang, W.M.[Wen-Min],
Better region proposals for pedestrian detection with R-CNN,
VCIP16(1-4)
IEEE DOI
1701
Feature extraction
BibRef
Zhang, Y.[Yuang],
He, H.Y.[Huan-Yu],
Li, J.G.[Jian-Guo],
Li, Y.X.[Yu-Xi],
See, J.[John],
Lin, W.Y.[Wei-Yao],
Variational Pedestrian Detection,
CVPR21(11617-11626)
IEEE DOI
2111
Computational modeling, Detectors,
Object detection, Propulsion, Inference algorithms, Pattern recognition
BibRef
Lin, B.Y.[Bo-Yao],
Chen, C.S.[Chu-Song],
Two Parallel Deep Convolutional Neural Networks for Pedestrian
Detection,
ICVNZ15(1-6)
IEEE DOI
1701
BibRef
Noman, M.,
Yousaf, M.H.[Muhammad Haroon],
Velastin, S.A.[Sergio A.],
An Optimized and Fast Scheme for Real-Time Human Detection Using
Raspberry Pi,
DICTA16(1-7)
IEEE DOI
1701
Detectors
BibRef
Westlake, N.[Nicholas],
Cai, H.P.[Hong-Ping],
Hall, P.[Peter],
Detecting People in Artwork with CNNs,
CVAA16(I: 825-841).
Springer DOI
1611
BibRef
Liu, L.H.[Li-Hang],
Lin, W.Y.[Wei-Yao],
Wu, L.S.[Li-Sheng],
Yu, Y.[Yong],
Yang, M.Y.[Michael Ying],
Unsupervised Deep Domain Adaptation for Pedestrian Detection,
Crowd16(II: 676-691).
Springer DOI
1611
BibRef
Zhang, L.L.[Li-Liang],
Lin, L.[Liang],
Liang, X.D.[Xiao-Dan],
He, K.M.[Kai-Ming],
Is Faster R-CNN Doing Well for Pedestrian Detection?,
ECCV16(II: 443-457).
Springer DOI
1611
BibRef
Hattori, H.[Hironori],
Boddeti, V.N.[Vishnu Naresh],
Kitani, K.[Kris],
Kanade, T.[Takeo],
Learning scene-specific pedestrian detectors without real data,
CVPR15(3819-3827)
IEEE DOI
1510
BibRef
Angelova, A.[Anelia],
Krizhevsky, A.[Alex],
Vanhoucke, V.[Vincent],
Ogale, A.[Abhijit],
Ferguson, D.[Dave],
Real-Time Pedestrian Detection with Deep Network Cascades,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Zhu, H.G.[Hai-Gang],
Chen, X.G.[Xiao-Gang],
Dai, W.Q.[Wei-Qun],
Fu, K.[Kun],
Ye, Q.X.[Qi-Xiang],
Jiao, J.B.[Jian-Bin],
Orientation robust object detection in aerial images using deep
convolutional neural network,
ICIP15(3735-3739)
IEEE DOI
1512
Aerial Object Detection
BibRef
Verma, A.,
Hebbalaguppe, R.,
Vig, L.,
Kumar, S.,
Hassan, E.,
Pedestrian Detection via Mixture of CNN Experts and Thresholded
Aggregated Channel Features,
ACVR15(555-563)
IEEE DOI
1602
Convolutional codes
BibRef
Sharma, P.[Pramod],
Nevatia, R.[Ram],
A Robust Adaptive Classifier for Detector Adaptation in a Video,
WACV15(921-928)
IEEE DOI
1503
BibRef
Earlier:
Efficient Detector Adaptation for Object Detection in a Video,
CVPR13(3254-3261)
IEEE DOI
PDF File.
1309
Boosting
BibRef
Sharma, P.[Pramod],
Nevatia, R.[Ramakant],
Multi class boosted random ferns for adapting a generic object
detector to a specific video,
WACV14(745-752)
IEEE DOI
1406
Boosting; Detectors; Manuals; Testing; Training; Training data; Vectors
BibRef
Sharma, P.[Pramod],
Huang, C.[Chang],
Nevatia, R.[Ram],
Efficient incremental learning of boosted classifiers for object
detection,
ICPR12(3248-3251).
WWW Link.
1302
BibRef
And:
Unsupervised incremental learning for improved object detection in a
video,
CVPR12(3298-3305).
IEEE DOI
1208
See also Vehicle detection from low quality aerial LIDAR data.
BibRef
Chen, X.G.[Xiao-Gang],
Wei, P.X.[Peng-Xu],
Ke, W.[Wei],
Ye, Q.X.[Qi-Xiang],
Jiao, J.B.[Jian-Bin],
Pedestrian Detection with Deep Convolutional Neural Network,
DeepLearnV14(354-365).
Springer DOI
1504
BibRef
Arteta, C.[Carlos],
Lempitsky, V.[Victor],
Noble, J.A.[J. Alison],
Zisserman, A.[Andrew],
Learning to Detect Partially Overlapping Instances,
CVPR13(3230-3237)
IEEE DOI
1309
All instances of a class in image. Cells or pedestrians.
BibRef
Sermanet, P.[Pierre],
Kavukcuoglu, K.[Koray],
Chintala, S.[Soumith],
Le Cun, Y.L.[Yann L.],
Pedestrian Detection with Unsupervised Multi-stage Feature Learning,
CVPR13(3626-3633)
IEEE DOI
1309
computer vision
BibRef
Yan, J.J.[Jun-Jie],
Yang, B.[Bin],
Lei, Z.[Zhen],
Li, S.Z.[Stan Z.],
Adaptive Structural Model for Video Based Pedestrian Detection,
ACCV14(I: 211-226).
Springer DOI
1504
See also Fastest Deformable Part Model for Object Detection, The.
BibRef
Yan, J.J.[Jun-Jie],
Zhang, X.C.[Xu-Cong],
Lei, Z.[Zhen],
Liao, S.C.[Sheng-Cai],
Li, S.Z.[Stan Z.],
Robust Multi-resolution Pedestrian Detection in Traffic Scenes,
CVPR13(3033-3040)
IEEE DOI
1309
DPM; Multi-Resolution; Multi-task Learning; Pedestrian Detection
BibRef
Wang, X.Y.[Xiao-Yu],
Cao, L.L.[Liang-Liang],
Feris, R.S.[Rogerio S.],
Data, A.[Ankur],
Han, T.X.[Tony X.],
Hierarchical Feature Pooling with Structure Learning:
A New Method for Pedestrian Detection,
SPTLI13(578-583)
IEEE DOI
1309
BibRef
Dikmen, M.[Mert],
Akbas, E.[Emre],
Huang, T.S.[Thomas S.],
Ahuja, N.[Narendra],
Pedestrian Recognition with a Learned Metric,
ACCV10(IV: 501-512).
Springer DOI
1011
BibRef
Li, L.Y.[Li-Yuan],
Leung, M.K.H.[Maylor K.H.],
Unsupervised learning of human perspective context using ME-DT for
efficient human detection in surveillance,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Sabzmeydani, P.[Payam],
Mori, G.[Greg],
Detecting Pedestrians by Learning Shapelet Features,
CVPR07(1-8).
IEEE DOI
0706
BibRef
Yang, T.[Tao],
Li, J.[Jing],
Pan, Q.[Quan],
Zhao, C.H.[Chun-Hui],
Zhu, Y.Q.[Yi-Qiang],
Active Learning Based Pedestrian Detection in Real Scenes,
ICPR06(IV: 904-907).
IEEE DOI
0609
BibRef
Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Local Features, LBP, Patterns, for Pedestrian Detection, People Detection .