17.1.3.2.1 Learning, Neural Nets for Human Detection, People Detection, Pedestrians

Chapter Contents (Back)
Human Detection. Pedestrian Detection. Neural Networks. Learning.
See also Human Detection, People Detection, Pedestrians, Locating.
See also Tracking People, Human Tracking, Pedestrian Tracking.

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

Saeidi, M.[Mahmoud], Ahmadi, A.[Ali],
A novel approach for deep pedestrian detection based on changes in camera viewing angle,
SIViP(14), No. 6, September 2020, pp. 1273-1281.
WWW Link. 2008
BibRef

Cai, Z.W.[Zhao-Wei], Saberian, M.[Mohammad], Vasconcelos, N.M.[Nuno M.],
Learning Complexity-Aware Cascades for Pedestrian Detection,
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], Shin, J.[Jitae], Oh, B.T.[Byung Tae],
Pedestrian detection using multi-scale squeeze-and-excitation module,
MVA(31), No. 6, August 2020, pp. Article55.
WWW Link. 2008
BibRef

Wang, X., Shen, C., Li, H., Xu, S.,
Human Detection Aided by Deeply Learned Semantic Masks,
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 Detection,
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, pedestrian detection BibRef

Wang, W.H.[Wen-Hao],
Detection of panoramic vision pedestrian based on deep learning,
IVC(103), 2020, pp. 103986.
Elsevier DOI 2011
Pedestrian detection, Panoramic vision, Switchable normalization, Convolutional neural networks, Deep learning BibRef

Wang, H., Li, Y., Wang, S.,
Fast Pedestrian Detection With Attention-Enhanced Multi-Scale RPN and Soft-Cascaded Decision Trees,
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., Li, J., Xiao, F., Li, Q., Yang, W., Han, J.,
Discriminative Multi-View Subspace Feature Learning for Action Recognition,
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], Ablavsky, V.[Vitaly], Wang, K.H.[Kai-Hong], Feng, Q.[Qi], Betke, M.[Margrit],
Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes,
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: A review,
IET-IPR(15), No. 2, 2021, pp. 286-301.
DOI Link 2106
Survey, Pedestrian Detection. BibRef

Zhao, Y.[Yang], Yu, X.H.[Xiao-Han], Gao, Y.S.[Yong-Sheng], Shen, C.H.[Chun-Hua],
Learning discriminative region representation for person retrieval,
PR(121), 2022, pp. 108229.
Elsevier DOI 2109
Person retrieval, Region representation BibRef

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 BibRef

Yao, Z.G.[Zong-Gui], Yu, J.[Jun], Ding, J.J.[Jia-Jun],
Contrastive learning of graph encoder for accelerating pedestrian trajectory prediction training,
IET-IPR(15), No. 14, 2021, pp. 3645-3660.
DOI Link 2112
BibRef

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 Detection in Fish-Eye Images,
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 BibRef

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 BibRef

Liang, Z.Y.[Zhi-Yuan], Guo, K.[Kan], Li, X.B.[Xiao-Bo], Jin, X.G.[Xiao-Gang], Shen, J.B.[Jian-Bing],
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 BibRef

Marnissi, M.A.[Mohamed Amine], Fradi, H.[Hajer], Sahbani, A.[Anis], Essoukri Ben Amara, N.[Najoua],
Unsupervised thermal-to-visible domain adaptation method for pedestrian detection,
PRL(153), 2022, pp. 222-231.
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, 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
BibRef

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


Molahasani, M.[Mahdiyar], Etemad, A.[Ali], Greenspan, M.[Michael],
Continual Learning for Out-of-Distribution Pedestrian Detection,
ICIP23(2685-2689)
IEEE DOI 2312
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
BibRef

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 .


Last update:Mar 16, 2024 at 20:36:19