17.1.3.2.6 Human Detection, People Detection, Pedestrians, Using Body Parts, Body Shape

Papageorgiou, C.P.[Constantine P.], Poggio, T.[Tomaso],
A Trainable System for Object Detection,
IJCV(38), No. 1, June 2000, pp. 15-33.
DOI Link 0006
BibRef
Earlier:
A Pattern Classification Approach to Dynamical Object Detection,
ICCV99(1223-1228).
IEEE DOI BibRef
And:
Trainable Pedestrian Detection,
ICIP99(IV:35-39).
IEEE DOI Object detection in Video using wavelets. BibRef

Papageorgiou, C.P.[Constantine P.],
A Trainable System for Object Detection in Images and Video Sequences,
MIT AI-TR-1685, May 2000. BibRef 0005 Ph.D.Thesis, May 2000.
WWW Link. 0105
BibRef

Mohan, A.[Anuj], Papageorgiou, C.P.[Constantine P.], Poggio, T.[Tomaso],
Example-Based Object Detection in Images by Components,
PAMI(23), No. 4, April 2001, pp. 349-361.
IEEE DOI 0001
Locate people in cluttered scenes by finding the 4 components -- head, legs, left arm, and right arm, then a second classifier is applied to exclude non-persons. BibRef

Papageorgiou, C.P.[Constantine P.], Evgeniou, T.[Theodoros], Poggio, T.[Tomaso],
A Trainable Object Detection System,
DARPA98(1019-1024). BibRef 9800
And:
A trainable pedestrian detection system,
IVS98(xx-yy). BibRef

Papageorgiou, C.P.[Constantine P.], Oren, M.[Michael], and Poggio, T.[Tomaso],
A General Framework for Object Detection,
ICCV98(555-562).
IEEE DOI BibRef 9800

Oren, M.[Michael], Papageorgiou, C.P.[Constantine P.], Sinha, P.[Pawan], Osuna, E.[Edgar], Poggio, T.[Tomaso],
Pedestrian Detection Using Wavelet Templates,
CVPR97(193-199).
IEEE DOI 9704
BibRef
And:
A Trainable System for People Detection,
DARPA97(207-214). Template average wavelet representations. vertical/horizontal/corner BibRef

Ioffe, S.[Sergey], Forsyth, D.A.[David A.],
Probabilistic Methods for Finding People,
IJCV(43), No. 1, June 2001, pp. 45-68.
DOI Link Or:
PDF File. 0108
BibRef
Earlier:
Finding People by Sampling,
ICCV99(1092-1097).
IEEE DOI Find candidate parts then assemble those that are consistent with being a person. Not all combinations can be tested, use a sampling approach. Segmentation into parts is the limiting factor. BibRef

Ioffe, S.[Sergey], Forsyth, D.A.[David A.],
Mixtures of Trees for Object Recognition,
CVPR01(II:180-185).
IEEE DOI 0110
BibRef
And:
Human Tracking with Mixtures of Trees,
ICCV01(I: 690-695).
IEEE DOI Or:
PDF File. 0106
BibRef

Tran, D.[Duan], Forsyth, D.A.[David A.],
Improved Human Parsing with a Full Relational Model,
ECCV10(IV: 227-240).
Springer DOI 1009
BibRef

Nanni, L., Lumini, A.,
Ensemble of Multiple Pedestrian Representations,
ITS(9), No. 2, June 2008, pp. 365-369.
IEEE DOI 0806
BibRef

Armanfard, N.[Narges], Komeili, M.[Majid], Kabir, E.[Ehsanollah],
TED: A texture-edge descriptor for pedestrian detection in video sequences,
PR(45), No. 3, March 2012, pp. 983-992.
Elsevier DOI 1111
Pedestrian detection; Texture; Edge; Local binary pattern; Block-based approach; Background subtraction; Surveillance systems BibRef

Tran, K.N.[Khai N.], Kakadiaris, I.A.[Ioannis A.], Shah, S.K.[Shishir K.],
Part-based motion descriptor image for human action recognition,
PR(45), No. 7, July 2012, pp. 2562-2572.
Elsevier DOI 1203
BibRef
Earlier:
Modeling Motion of Body Parts for Action Recognition,
BMVC11(xx-yy).
HTML Version. 1110
Human action recognition; Motion descriptor image; Subspace learning; Principal component analysis; Discriminant analysis; Sparse representation BibRef

Andriluka, M.[Mykhaylo], Roth, S.[Stefan], Schiele, B.[Bernt],
Discriminative Appearance Models for Pictorial Structures,
IJCV(99), No. 3, September 2012, pp. 259-280.
WWW Link. 1206
BibRef
Earlier:
Monocular 3D pose estimation and tracking by detection,
CVPR10(623-630).
IEEE DOI 1006
Video of talk:
WWW Link. BibRef
Earlier:
Pictorial structures revisited: People detection and articulated pose estimation,
CVPR09(1014-1021).
IEEE DOI 0906
BibRef
Earlier:
People-tracking-by-detection and people-detection-by-tracking,
CVPR08(1-8).
IEEE DOI 0806

See also 3D Pictorial Structures Revisited: Multiple Human Pose Estimation. BibRef

Andriluka, M.[Mykhaylo], Sigal, L.[Leonid],
Human Context: Modeling Human-Human Interactions for Monocular 3D Pose Estimation,
AMDO12(260-272).
Springer DOI 1208
BibRef

Tang, S.[Siyu], Andriluka, M.[Mykhaylo], Schiele, B.[Bernt],
Detection and Tracking of Occluded People,
IJCV(110), No. 1, October 2014, pp. 58-69.
Springer DOI 1410
BibRef
Earlier: BMVC12(9).
DOI Link 1301
Award, BMVC. BibRef

Tang, S.[Siyu], Andriluka, M.[Mykhaylo], Andres, B.[Bjoern], Schiele, B.[Bernt],
Multiple People Tracking by Lifted Multicut and Person Re-identification,
CVPR17(3701-3710)
IEEE DOI 1711
BibRef
Earlier: A1, A3, A2, A4:
Multi-person Tracking by Multicut and Deep Matching,
MOTC16(II: 100-111).
Springer DOI 1611
Benchmark testing, Image edge detection, Mathematical model, Optimization, Target tracking, Trajectory BibRef

Tang, S.[Siyu], Andriluka, M.[Mykhaylo], Milan, A.[Anton], Schindler, K.[Konrad], Roth, S.[Stefan], Schiele, B.[Bernt],
Learning People Detectors for Tracking in Crowded Scenes,
ICCV13(1049-1056)
IEEE DOI 1403
Multiple People Tracking BibRef

Pishchulin, L.[Leonid], Jain, A.[Arjun], Andriluka, M.[Mykhaylo], Thormahlen, T.[Thorsten], Schiele, B.[Bernt],
Articulated people detection and pose estimation: Reshaping the future,
CVPR12(3178-3185).
IEEE DOI 1208
BibRef

Wojek, C.[Christian], Schiele, B.[Bernt],
A Performance Evaluation of Single and Multi-feature People Detection,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Bhattacharyya, A., Fritz, M.[Mario], Schiele, B.[Bernt],
Long-Term On-board Prediction of People in Traffic Scenes Under Uncertainty,
CVPR18(4194-4202)
IEEE DOI 1812
Pattern recognition BibRef

Seemann, E.[Edgar], Fritz, M.[Mario], Schiele, B.[Bernt],
Towards Robust Pedestrian Detection in Crowded Image Sequences,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Seemann, E.[Edgar], Schiele, B.[Bernt],
Cross-Articulation Learning for Robust Detection of Pedestrians,
DAGM06(242-252).
Springer DOI 0610
Award, GCPR, HM. BibRef

Seemann, E.[Edgar], Leibe, B.[Bastian], Schiele, B.[Bernt],
Multi-Aspect Detection of Articulated Objects,
CVPR06(II: 1582-1588).
IEEE DOI 0606
BibRef
Earlier: A2, A1, A3:
Pedestrian Detection in Crowded Scenes,
CVPR05(I: 878-885).
IEEE DOI 0507
Pedestrians. BibRef

Seemann, E., Leibe, B., Mikolajczyk, K.[Krystian], Schiele, B.,
An Evaluation of Local Shape-Based Features for Pedestrian Detection,
BMVC05(xx-yy).
HTML Version. 0509
BibRef

Li, S.F.[Shi-Feng], Lu, H.C.[Hu-Chuan], Zhang, L.[Lei],
Arbitrary body segmentation in static images,
PR(45), No. 9, September 2012, pp. 3402-3413.
Elsevier DOI 1206
Pictorial structure; Superpixel based EM algorithm; L1 based graph cuts Human body from static images. BibRef

Ma, L.Y.[Lian-Yang], Yang, X.K.[Xiao-Kang], Xu, Y.[Yi], Zhu, J.[Jun],
A generalized EMD with body prior for pedestrian identification,
JVCIR(24), No. 6, August 2013, pp. 708-716.
Elsevier DOI 1306
BibRef
Earlier:
Human identification using body prior and generalized EMD,
ICIP11(1441-1444).
IEEE DOI 1201
EMD; Generalized EMD; Pedestrian identification; Body prior; Probabilistic map; Matching metric; KL divergence; Learning weight BibRef

Lo, K.H.[Kuo-Hua], Chuang, J.H.[Jen-Hui],
Vanishing Point-based Line Sampling for Real-time People Localization,
CirSysVideo(23), No. 7, 2013, pp. 1209-1223.
IEEE DOI 1307
BibRef
Earlier:
View-invariant measure of line correspondence and its application in people localization,
ICIP12(1985-1988).
IEEE DOI 1302
BibRef
Earlier:
Vanishing point-based line sampling for efficient axis-based people localization,
ICIP11(521-524).
IEEE DOI 1201
filtering theory BibRef

Liu, C.W., Chen, H.T., Lo, K.H.[Kuo-Hua], Wang, C.J., Chuang, J.H.[Jen-Hui],
Accelerating Vanishing Point-Based Line Sampling Scheme for Real-Time People Localization,
CirSysVideo(27), No. 3, March 2017, pp. 409-420.
IEEE DOI 1703
Adaptation models BibRef

Lin, Y.S.[Yen-Shuo], Lo, K.H.[Kuo-Hua], Chen, H.T.[Hua-Tsung], Chuang, J.H.[Jen-Hui],
Vanishing Point-Based Image Transforms for Enhancement of Probabilistic Occupancy Map-Based People Localization,
IP(23), No. 12, December 2014, pp. 5586-5598.
IEEE DOI 1402
cameras BibRef

Vansteenberge, J.[Jarich], Mukunoki, M.[Masayuki], Minoh, M.[Michihiko],
Improving Hough Based Pedestrian Detection Accuracy by Using Segmentation and Pose Subspaces,
IEICE(E97-D), No. 10, October 2014, pp. 2760-2768.
WWW Link. 1411
BibRef

Xu, J.L.[Jiao-Long], Vazquez, D., Lopez, A.M.[Antonio M.], Marin, J., Ponsa, D.[Daniel],
Learning a Part-Based Pedestrian Detector in a Virtual World,
ITS(15), No. 5, October 2014, pp. 2121-2131.
IEEE DOI 1410
accident prevention BibRef

Vazquez, D.[David], Xu, J.L.[Jiao-Long], Ramos, S.[Sebastian], Lopez, A.M.[Antonio M.], Ponsa, D.[Daniel],
Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes,
GT13(706-711)
IEEE DOI 1309
ADAS;Domain Adaptation;Pedestrian Detection BibRef

Xu, J.L.[Jiao-Long], Vazquez, D.[David], Ramos, S.[Sebastian], Lopez, A.M.[Antonio M.], Ponsa, D.[Daniel],
Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers,
GT13(688-693).
IEEE DOI 1309
LDA BibRef

Delibasis, K.K., Plagianakos, V.P., Maglogiannis, I.,
Refinement of human silhouette segmentation in omni-directional indoor videos,
CVIU(128), No. 1, 2014, pp. 65-83.
Elsevier DOI 1410
Video segmentation BibRef

Al-Maadeed, S.[Somaya], Almotaeryi, R.[Resheed], Jiang, R.[Richard], Bouridane, A.[Ahmed],
Robust human silhouette extraction with Laplacian fitting,
PRL(49), No. 1, 2014, pp. 69-76.
Elsevier DOI 1410
Human silhouette BibRef

Liu, W., Yu, B., Duan, C., Chai, L., Yuan, H., Zhao, H.,
A Pedestrian-Detection Method Based on Heterogeneous Features and Ensemble of Multi-View Pose Parts,
ITS(16), No. 2, April 2015, pp. 813-824.
IEEE DOI 1504
Detectors BibRef

Takarli, F.[Fariba], Aghagolzadeh, A.[Ali], Seyedarabi, H.[Hadi],
Combination of high-level features with low-level features for detection of pedestrian,
SIViP(10), No. 1, January 2016, pp. 93-101.
WWW Link. 1601
BibRef

Kim, K.[Kyuwon], Sohn, K.H.[Kwang-Hoon],
Real-time Human Detection based on Personness Estimation,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., Metaxas, D.N., Zhou, X.S.,
Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition,
MedImg(35), No. 5, May 2016, pp. 1332-1343.
IEEE DOI 1605
Algorithm design and analysis BibRef

Liu, J.J.[Jing-Jing], Zhang, S.T.[Shao-Ting], Wang, S.[Shu], Metaxas, D.M.[Dimitris M.],
Multispectral Deep Neural Networks for Pedestrian Detection,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Cao, J., Pang, Y., Li, X.,
Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry,
IP(25), No. 12, December 2016, pp. 5538-5551.
IEEE DOI 1612
BibRef
Earlier: CVPR16(1316-1324)
IEEE DOI 1612
object detection BibRef

Cao, J., Pang, Y., Li, X.,
Learning Multilayer Channel Features for Pedestrian Detection,
IP(26), No. 7, July 2017, pp. 3210-3220.
IEEE DOI 1706
Computational modeling, Feature extraction, Image resolution, Neural networks, Object detection, Proposals, CNN, HOG+LUV, NMS, Pedestrian detection, multi-layer, channel, features, (MCF) BibRef

Hariharan, B.[Bharath], Arbeláez, P.[Pablo], Girshick, R.[Ross], Malik, J.[Jitendra],
Object Instance Segmentation and Fine-Grained Localization Using Hypercolumns,
PAMI(39), No. 4, April 2017, pp. 627-639.
IEEE DOI 1703
BibRef
Earlier:
Hypercolumns for object segmentation and fine-grained localization,
CVPR15(447-456)
IEEE DOI 1510
Image segmentation
See also Indoor Scene Understanding with RGB-D Images: Bottom-up Segmentation, Object Detection and Semantic Segmentation. BibRef

Alcázar, J.L.[Juan León], Heilbron, F.C.[Fabian Caba], Mai, L.[Long], Perazzi, F.[Federico], Lee, J.Y.[Joon-Young], Arbeláez, P.[Pablo], Ghanem<", B.[Bernard], /A1>,
APES: Audiovisual Person Search in Untrimmed Video,
MULA21(1720-1729)
IEEE DOI 2109
Visualization, Annotations, Streaming media, Benchmark testing, Search problems BibRef

Gkioxari, G.[Georgia], Malik, J.[Jitendra],
Finding action tubes,
CVPR15(759-768)
IEEE DOI 1510
BibRef

Gkioxari, G.[Georgia], Girshick, R.[Ross], Malik, J.[Jitendra],
Actions and Attributes from Wholes and Parts,
ICCV15(2470-2478)
IEEE DOI 1602
BibRef
And:
Contextual Action Recognition with R*CNN,
ICCV15(1080-1088)
IEEE DOI 1602
Birds. BibRef

Gkioxari, G.[Georgia], Hariharan, B.[Bharath], Girshick, R.[Ross], Malik, J.[Jitendra],
Using k-Poselets for Detecting People and Localizing Their Keypoints,
CVPR14(3582-3589)
IEEE DOI 1409
BibRef

Li, M.[Meng], Leung, H.[Howard],
Graph-based approach for 3D human skeletal action recognition,
PRL(87), No. 1, 2017, pp. 195-202.
Elsevier DOI 1703
Action recognition BibRef

Huang, L.[Lei], Nie, J.[Jie], Wei, Z.Q.[Zhi-Qiang],
Human body segmentation based on shape constraint,
MVA(28), No. 7, October 2017, pp. 715-724.
WWW Link. 1710
BibRef

Wang, S., Cheng, J., Liu, H., Wang, F., Zhou, H.,
Pedestrian Detection via Body Part Semantic and Contextual Information With DNN,
MultMed(20), No. 11, November 2018, pp. 3148-3159.
IEEE DOI 1810
Semantics, Detectors, Feature extraction, Machine learning, Object detection, Proposals, Bidirectional control, adaptive context selection BibRef

Zhou, C.L.[Chun-Luan], Yuan, J.S.[Jun-Song],
Multi-label learning of part detectors for occluded pedestrian detection,
PR(86), 2019, pp. 99-111.
Elsevier DOI 1811
BibRef
Earlier:
Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection,
ICCV17(3506-3515)
IEEE DOI 1802
BibRef
Earlier:
Learning to Integrate Occlusion-Specific Detectors for Heavily Occluded Pedestrian Detection,
ACCV16(II: 305-320).
Springer DOI 1704
BibRef
Earlier:
Non-rectangular Part Discovery for Object Detection,
BMVC14(xx-yy).
HTML Version. 1410
BibRef
Earlier:
Arbitrary-Shape Object Localization Using Adaptive Image Grids,
ACCV12(I:71-84).
Springer DOI 1304
Pedestrian detection, Part detectors, Multi-label learning, Occlusion handling, Detector integration, Context. decision trees, learning (artificial intelligence), object detection, pedestrians, traffic engineering computing, Training BibRef

Zhao, Y., Yuan, Z., Chen, B.,
Accurate Pedestrian Detection by Human Pose Regression,
IP(29), 2020, pp. 1591-1605.
IEEE DOI 1911
Feature extraction, Detectors, Pose estimation, Task analysis, Shape, Decision trees, Pedestrian detection, cascade decision trees BibRef

Yang, P., Zhang, G., Wang, L., Xu, L., Deng, Q., Yang, M.H.,
A Part-Aware Multi-Scale Fully Convolutional Network for Pedestrian Detection,
ITS(22), No. 2, February 2021, pp. 1125-1137.
IEEE DOI 2102
Detectors, Feature extraction, Proposals, Semantics, Intelligent transportation systems, Object detection, Buildings, fully convolutional network BibRef

Zhang, S., Qi, G.J., Cao, X., Song, Z., Zhou, J.,
Human Parsing With Pyramidical Gather-Excite Context,
CirSysVideo(31), No. 3, March 2021, pp. 1016-1030.
IEEE DOI 2103
Task analysis, Semantics, Aggregates, Lips, Clothing, Encoding, Human parsing, Pyramid Spatial Parsing (PSP), context encoding BibRef

Chen, Z.[Zhe], Ouyang, W.L.[Wan-Li], Liu, T.L.[Tong-Liang], Tao, D.C.[Da-Cheng],
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection,
IJCV(129), No. 4, April 2021, pp. 1121-1138.
Springer DOI 2104
BibRef

Zhao, Y.[Yang], Shen, C.H.[Chun-Hua], Yu, X.H.[Xiao-Han], Chen, H.[Hao], Gao, Y.S.[Yong-Sheng], Xiong, S.W.[Sheng-Wu],
Learning deep part-aware embedding for person retrieval,
PR(116), 2021, pp. 107938.
Elsevier DOI 2106
Person retrieval, Part-aware embedding, Improved triplet loss BibRef

Chu, H.Z.[Hua-Zhen], Ma, H.M.[Hui-Min], Li, X.[Xi],
Pedestrian instance segmentation with prior structure of semantic parts,
PRL(149), 2021, pp. 9-16.
Elsevier DOI 2108
Pedestrian instance segmentation, Occlusion, Semantic parts, Pedestrian detection BibRef

Ruan, B.J.[Bin-Jie], Zhang, C.Y.[Chong-Yang],
Occluded pedestrian detection combined with semantic features,
IET-IPR(15), No. 10, 2021, pp. 2292-2300.
DOI Link 2108
BibRef

He, Y.[Ye], Zhu, C.[Chao], Yin, X.C.[Xu-Cheng],
Occluded Pedestrian Detection via Distribution-Based Mutual-Supervised Feature Learning,
ITS(23), No. 8, August 2022, pp. 10514-10529.
IEEE DOI 2208
BibRef
Earlier:
Mutual-Supervised Feature Modulation Network for Occluded Pedestrian Detection,
ICPR21(8453-8460)
IEEE DOI 2105
Detectors, Feature extraction, Annotations, Training, Standards, Proposals, Task analysis, Pedestrian detection, occlusion handling, visible body information. Modulation. BibRef

Abdelmutalab, A.[Ameen], Wang, C.Y.[Chun-Yan],
Pedestrian Detection Using MB-CSP Model and Boosted Identity Aware Non-Maximum Suppression,
ITS(23), No. 12, December 2022, pp. 24454-24463.
IEEE DOI 2212
Feature extraction, Detectors, Unified modeling language, Complexity theory, Task analysis, Convolution, Training, part fusing BibRef

Althoupety, A.[Afnan], Wang, L.Y.[Li-Yun], Feng, W.C.[Wu-Chi], Rekabdar, B.[Banafsheh],
DaFF: Dual Attentive Feature Fusion for Multispectral Pedestrian Detection,
PBVS24(2997-3006)
IEEE DOI 2410
Degradation, Pedestrians, Attention mechanisms, Lighting, Feature extraction, Multispectral Pedestrian Detection, Attention Mechanisms BibRef

Terreran, M.[Matteo], Evangelista, D.[Daniele], Lazzaro, J.[Jacopo], Pretto, A.[Alberto],
Make It Easier: An Empirical Simplification of a Deep 3D Segmentation Network for Human Body Parts,
CVS21(144-156).
Springer DOI 2109
BibRef

Xie, X., Wang, Z.,
Multi-scale Semantic Segmentation Enriched Features for Pedestrian Detection,
ICPR18(2196-2201)
IEEE DOI 1812
Feature extraction, Detectors, Training, Object detection, Semantics, Feeds, Convolutional neural networks, pedestrian detection, convolutional neural network BibRef

Tan, F., Bernier, C., Cohen, B., Ordonez, V., Barnes, C.,
Where and Who? Automatic Semantic-Aware Person Composition,
WACV18(1519-1528)
IEEE DOI 1806
convolution, feature extraction, feedforward neural nets, image representation, image segmentation, Task analysis BibRef

Yu, H., Ohn-Bar, E., Yoo, D., Kitani, K.M.,
SmartPartNet: Part-Informed Person Detection for Body-Worn Smartphones,
WACV18(1103-1112)
IEEE DOI 1806
image motion analysis, mobile computing, neural nets, object detection, smart phones, wearable computers, Training BibRef

Chen, G., Cai, X., Han, H., Shan, S., Chen, X.,
HeadNet: Pedestrian Head Detection Utilizing Body in Context,
FG18(556-563)
IEEE DOI 1806
Feature extraction, Head, Object detection, Proposals, Robustness, Semantics, Training, Body in Context, Head detection, Semantic feature fusion BibRef

Kong, W.J.[Wei-Jie], Li, N.N.[Nan-Nan], Li, T.H.[Thomas H.], Li, G.[Ge],
Deep Pedestrian Detection Using Contextual Information and Multi-level Features,
MMMod18(I:166-177).
Springer DOI 1802
BibRef

Zheng, Q.[Qi], Chen, J.[Jun], Jiang, J.J.[Jun-Jun], Hu, R.M.[Rui-Min],
Reinforcing Pedestrian Parsing on Small Scale Dataset,
MMMod18(I:417-427).
Springer DOI 1802
BibRef

Jiang, H., Grauman, K.[Kristen],
Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly,
CVPR17(3435-3443)
IEEE DOI 1711
Detectors, Image segmentation, Legged locomotion, Optimization, Proposals, Semantics, Torso BibRef

Gong, K., Liang, X., Zhang, D., Shen, X., Lin, L.,
Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing,
CVPR17(6757-6765)
IEEE DOI 1711
Benchmark testing, Image segmentation, Lips, Neural networks, Semantics, Servers, Training BibRef

Shi, L.[Liu], Liu, J.H.[Jia-Hang], Wang, Y.H.[Yi-Hao],
A universal pedestrian's foot-point and head-point recognition with improved motion detection algorithm,
ICIVC17(281-287)
IEEE DOI 1708
Brightness, Feature extraction, Image color analysis, Lighting, Matrix decomposition, Motion detection, Motion segmentation, foot-point, head-point, motion detection, video, surveillance BibRef

Zhao, Y.[Yun], Yuan, Z.J.[Ze-Jian], Chen, D.P.[Da-Peng], Lyu, J.[Jie], Liu, T.[Tie],
Fast Pedestrian Detection via Random Projection Features with Shape Prior,
WACV17(962-970)
IEEE DOI 1609
Decision trees, Feature extraction, Image color analysis, Shape, Testing, Training, Vegetation BibRef

Yamashita, T., Fukui, H., Yamauchi, Y., Fujiyoshi, H.,
Pedestrian and part position detection using a regression-based multiple task deep convolutional neural network,
ICPR16(3500-3505)
IEEE DOI 1705
Cameras, Estimation, Feature extraction, Head, Legged locomotion, Neural networks, Training BibRef

Popa, A.I.[Alin-Ionut], Sminchisescu, C.[Cristian],
Parametric Image Segmentation of Humans with Structural Shape Priors,
ACCV16(II: 68-83).
Springer DOI 1704
BibRef

Xia, F.T.[Fang-Ting], Wang, P.[Peng], Chen, L.C.[Liang-Chieh], Yuille, A.L.[Alan L.],
Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net,
ECCV16(V: 648-663).
Springer DOI 1611
BibRef

Chen, Q., Jiang, W., Zhao, Y., Zhao, Z.,
Part-based deep network for pedestrian detection in surveillance videos,
VCIP15(1-4)
IEEE DOI 1605
Airports BibRef

Zheng, Y.[Ying], Yao, H.X.[Hong-Xun], Sun, X.S.[Xiao-Shuai], Zhao, S.C.[Si-Cheng],
Distinctive action sketch,
ICIP15(576-580)
IEEE DOI 1512
Action Sketch; Objective-ness; Sketchability; Spatio-Temporal Consistency BibRef

Mao, X.J.[Xiao-Jiao], Zhao, J.Y.[Jiu-Yang], Yang, Y.B.[Yu-Bin], Li, N.[Ning],
Enhanced deformable part model for pedestrian detection via joint state inference,
ICIP15(941-945)
IEEE DOI 1512
Pedestrian detection BibRef

Chen, X.J.[Xian-Jie], Yuille, A.L.[Alan L.],
Parsing occluded people by flexible compositions,
CVPR15(3945-3954)
IEEE DOI 1510
BibRef

Liu, S.[Si], Liang, X.D.[Xiao-Dan], Liu, L.Q.[Luo-Qi], Shen, X.H.[Xiao-Hui], Yang, J.C.[Jian-Chao], Xu, C.S.[Chang-Sheng], Lin, L.[Liang], Cao, X.C.[Xiao-Chun], Yan, S.C.[Shui-Cheng],
Matching-CNN meets KNN: Quasi-parametric human parsing,
CVPR15(1419-1427)
IEEE DOI 1510
BibRef

Kim, H.K.[Hak Kyoung], Kim, Y.H.[Yong-Hyun], Kim, D.J.[Dai-Jin],
Adaptive Deformation Handling for Pedestrian Detection,
WACV15(156-161)
IEEE DOI 1503
Boosting BibRef

Shaukat, A.[Affan], Gilbert, A.[Andrew], Windridge, D.[David], Bowden, R.[Richard],
Meeting in the Middle: A top-down and bottom-up approach to detect pedestrians,
ICPR12(874-877).
WWW Link. 1302
BibRef

Schiel, J., Green, R.,
Adaptive human silhouette extraction with chromatic distortion and contour tracking,
IVCNZ13(288-292)
IEEE DOI 1402
computer vision BibRef

Zweng, A.[Andreas], Kampel, M.[Martin],
Performance evaluation of an improved relational feature model for pedestrian detection,
PETS13(53-60)
IEEE DOI 1411
BibRef
And:
Introducing a Inter-frame Relational Feature Model for Pedestrian Detection,
SCIA13(225-235).
Springer DOI 1311
BibRef
Earlier:
Improved Relational Feature Model for People Detection Using Histogram Similarity Functions,
AVSS12(422-427).
IEEE DOI 1211
BibRef
Earlier:
Introducing Confidence Maps to Increase the Performance of Person Detectors,
ISVC11(II: 446-455).
Springer DOI 1109
BibRef
Earlier:
Unexpected Human Behavior Recognition in Image Sequences Using Multiple Features,
ICPR10(368-371).
IEEE DOI 1008
correlation methods
See also Introducing a Statistical Behavior Model into Camera-Based Fall Detection. BibRef

Yao, C.[Cong], Bai, X.[Xiang], Liu, W.Y.[Wen-Yu], Latecki, L.J.[Longin Jan],
Human Detection Using Learned Part Alphabet and Pose Dictionary,
ECCV14(V: 251-266).
Springer DOI 1408
BibRef

Zhu, Y.[Yu], Chen, W.B.[Wen-Bin], Guo, G.D.[Guo-Dong],
Fusing Spatiotemporal Features and Joints for 3D Action Recognition,
HAU3D13(486-491)
IEEE DOI 1309
3D action recognition;Human action recognition;fusion BibRef

Wang, M.[Meng], Zhang, Z.X.[Zhao-Xiang], Wang, Y.H.[Yun-Hong],
Efficient Human Parsing Based on Sketch Representation,
ACCV12(I:396-407).
Springer DOI 1304
BibRef

Liang, J.X.[Ji-Xiang], Ye, Q.X.[Qi-Xiang], Chen, J.[Jie], Jiao, J.B.[Jian-Bin],
Evaluation of local feature descriptors and their combination for pedestrian representation,
ICPR12(2496-2499).
WWW Link. 1302
BibRef

Gao, W.[Wen], Chen, X.G.[Xiao-Gang], Ye, Q.X.[Qi-Xiang], Jiao, J.B.[Jian-Bin],
Pedestrian detection via part-based topology model,
ICIP12(445-448).
IEEE DOI 1302
BibRef

Jacques, J.C.S.[Julio C.S.], Jung, C.R.[Claudio R.], Musse, S.R.[Soraia R.],
Head-shoulder human contour estimation in still images,
ICIP14(278-282)
IEEE DOI 1502
Computational modeling BibRef

Jacques, J.C.S.[Julio C.S.], Dihl, L.L.[Leandro L.], Jung, C.R.[Claudio R.], Musse, S.R.[Soraia R.],
Self-occlusion and 3D pose estimation in still images,
ICIP13(2539-2543)
IEEE DOI 1402
3D pose estimation BibRef

Jacques Junior, J.C.S.[Julio C. S.], Jung, C.R.[Claudio R.], Musse, S.R.[Soraia R.],
Skeleton-based human segmentation in still images,
ICIP12(141-144).
IEEE DOI 1302
BibRef

Kohlschütter, T.[Tomáš], Herout, P.[Pavel],
Automatic Human Body Parts Detection in a 2d Anthropometric System,
ISVC12(II: 536-544).
Springer DOI 1209
BibRef

Rao, S.[Supriya], Pramod, N.C., Paturu, C.K.[Chaitanya Krishna],
People detection in image and video data,
VNBA08(85-92).
DOI Link 1208
Combines a generative model with a discriminative model. BibRef

Rothrock, B.[Brandon], Zhu, S.C.[Song-Chun],
Human parsing using stochastic and-or grammars and rich appearances,
SIG11(640-647).
IEEE DOI 1201
BibRef

Cao, S.[Song], Duan, G.Q.[Gen-Quan], Hai-Zhou, A.I.,
Fast human detection using Node-Combined Part Detector,
ICIP11(3589-3592).
IEEE DOI 1201
BibRef

Li, K.C.[Ke-Chun], Su, H.R.[Hong-Ren], Lai, S.H.[Shang-Hong],
Pedestrian Image Segmentation via Shape-Prior Constrained Random Walks,
PSIVT11(II: 215-226).
Springer DOI 1111
BibRef

Pishchulin, L.[Leonid], Jain, A.[Arjun], Wojek, C.[Christian], Thormaehlen, T.[Thorsten], Schiele, B.[Bernt],
In Good Shape: Robust People Detection based on Appearance and Shape,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Anwer, R.M.[Rao Muhammad], Vázquez, D.[David], López, A.M.[Antonio M.],
Color Contribution to Part-Based Person Detection in Different Types of Scenarios,
CAIP11(II: 463-470).
Springer DOI 1109
BibRef
And:
Opponent Colors for Human Detection,
IbPRIA11(363-370).
Springer DOI 1106
BibRef

Bo, Y.H.[Yi-Hang], Fowlkes, C.C.[Charless C.],
Shape-based pedestrian parsing,
CVPR11(2265-2272).
IEEE DOI 1106
BibRef

Tang, S.P.[Shao-Peng], Goto, S.[Satoshi],
Multi scale block histogram of template feature for pedestrian detection,
ICIP10(3493-3496).
IEEE DOI 1009
BibRef

Huang, Y.Z.[Yong-Zhen], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
A Heuristic Deformable Pedestrian Detection Method,
ACCV10(II: 542-553).
Springer DOI 1011
BibRef

Ando, H.[Hiroaki], Fujiyoshi, H.[Hironobu],
Human-Area Segmentation by Selecting Similar Silhouette Images Based on Weak-Classifier Response,
ICPR10(3444-3447).
IEEE DOI 1008
BibRef

John, G.S.[Gladis S.], West, G.A.W.[Geoff A. W.], Lazarescu, M.[Mihai],
Part Based Recognition of Pedestrians Using Multiple Features and Random Forests,
DICTA10(363-368).
IEEE DOI 1012
BibRef

Wang, S.[Sheng], Du, R.[Ruo], Wu, Q.A.[Qi-Ang], He, X.J.[Xiang-Jian],
Adaptive Stick-Like Features for Human Detection Based on Multi-scale Feature Fusion Scheme,
DICTA10(375-380).
IEEE DOI 1012
BibRef

Bar-Hillel, A.[Aharon], Levi, D.[Dan], Krupka, E.[Eyal], Goldberg, C.[Chen],
Part-Based Feature Synthesis for Human Detection,
ECCV10(IV: 127-142).
Springer DOI 1009
BibRef

Chen, Y.T.[Yu-Ting], Chen, C.S.[Chu-Song], Hung, Y.P.[Yi-Ping], Chang, K.Y.[Kuang-Yu],
Multi-class multi-instance boosting for part-based human detection,
VS09(1177-1184).
IEEE DOI 0910
BibRef

Nguyen, D.T.[Duc Thanh], Li, W.Q.[Wan-Qing], Ogunbona, P.[Philip],
A novel template matching method for human detection,
ICIP09(2549-2552).
IEEE DOI 0911
BibRef
And:
A part-based template matching method for multi-view human detection,
IVCNZ09(357-362).
IEEE DOI 0911
BibRef

Zhou, C.H.[Chen-Hui], Tang, L.[Liang], Wang, S.J.[Sheng-Jin], Ding, X.Q.[Xiao-Qing],
Human Detection Based on Fusion of Histograms of Oriented Gradients and Main Partial Features,
CISP09(1-5).
IEEE DOI 0910
BibRef

Barnard, M.[Mark], Heikkilä, J.[Janne],
On Bin Configuration of Shape Context Descriptors in Human Silhouette Classification,
ACIVS08(xx-yy).
Springer DOI 0810
BibRef

Zhao, S.L.[San-Lung], Lee, H.J.[Hsi-Jian],
Human Silhouette Extraction based on HMM,
ICPR06(II: 994-997).
IEEE DOI 0609
BibRef

Ahn, J.H.[Jung-Ho], Byun, H.R.[Hye-Ran],
Human Silhouette Extraction Method Using Region Based Background Subtraction,
MIRAGE07(412-420).
Springer DOI 0703
BibRef

Mikolajczyk, K.[Krystian], Schmid, C.[Cordelia], Zisserman, A.[Andrew],
Human Detection Based on a Probabilistic Assembly of Robust Part Detectors,
ECCV04(Vol I: 69-82).
Springer DOI 0405
BibRef

Lee, L., Dalley, G., Tieu, K.,
Learning pedestrian models for silhouette refinement,
ICCV03(663-670).
IEEE DOI 0311
BibRef

Ballerini, L.[Lucia],
Multiple Genetic Snakes for People Segmentation in Video Sequences,
SCIA03(275-282).
Springer DOI 0310
BibRef

Leo, M., Spagnolo, P., Attolico, G., Distante, A.,
Shape Based People Detection for Visual Surveillance Systems,
AVBPA03(285-293).
Springer DOI 0310
BibRef

Yamada, M., Ebihara, K., Ohya, J.,
New Robust Real-Time Method for Extracting Human Silhouettes from Color Images,
AFGR98(528-533).
IEEE DOI BibRef 9800

Ronfard, R., Schmid, C., Triggs, B.,
Learning to Parse Pictures of People,
ECCV02(IV: 700 ff.).
Springer DOI 0205
BibRef

Stauffer, C.[Chris], Antone, M.,
Translation Templates for Object Matching Across Predictable Pose Variation,
BMVC06(III:219).
PDF File. 0609
BibRef

Stauffer, C., Grimson, W.E.L.,
Similarity Templates for Detection and Recognition,
CVPR01(I:221-228).
IEEE DOI 0110
How to represent pedestrians, region based BibRef

Kinzel, W.,
Pedestrian Recognition by Modelling their Shapes and Movements,
IAP-III1994, pp. 547-554. BibRef 9400

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Pedestrian Attributes, Pedestrian Descriptions .