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

Chapter Contents (Back)
Human Detection. Pedestrian Detection. Neural Networks. 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

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

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

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

Wang, K., Lin, L., Zuo, W., Gu, S., Zhang, L.,
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., Ouyang, W.L.[Wan-Li], Li, H.S.[Hong-Sheng], Wang, X.G.[Xiao-Gang],
Object Detection from Video Tubelets with Convolutional Neural Networks,
CVPR16(817-825)
IEEE DOI 1612
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, Neural networks, Object detection, Proposals, Tracking, Videos, Visualization 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

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

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

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.,
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, 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

Wang, J.Y.[Jing-Ya], Zhu, X.T.[Xia-Tian], Gong, S.G.[Shao-Gang], Li, W.[Wei],
Attribute Recognition by Joint Recurrent Learning of Context and Correlation,
ICCV17(531-540)
IEEE DOI 1802
few training samples, noise, clutter. Pedestrians. computer vision, image colour analysis, learning (artificial intelligence), object detection, Surveillance 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
computer vision, 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, Computer architecture, Computer vision, Neural networks, Semantics, Training 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

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
Computer architecture, Computer vision, 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, Three-dimensional, displays 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, Computer vision, 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
Computer architecture, 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, Computer architecture, 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

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
computer vision 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.[Lihang], Lin, W.Y.[Wei-Yao], Wu, L.[Lisheng], 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

Cai, Z., Saberian, M., Vasconcelos, N.M.,
Learning Complexity-Aware Cascades for Deep Pedestrian Detection,
ICCV15(3361-3369)
IEEE DOI 1602
Algorithm design and analysis 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.[Weiqun], 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.[Xiaoyu], Cao, L.L.[Liang-Liang], Feris, R.[Rogerio], 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 -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
HoG, Gradients, Histogram of Gradients for Human Detection, People Detection, Pedestrians .


Last update:Oct 10, 2018 at 21:21:08