16.7.4.2 Human Detection, People Detection, Pedestrians, Locating

Chapter Contents (Back)
Human Detection. Pedestrian Detection. See also Learning, Neural Nets for Human Detection, People Detection, Pedestrians. HoG Based: See also HoG, Gradients, Histogram of Gradients for Human Detection, People Detection, Pedestrians. Depth based: See also Human Detection, People Detection, Pedestrians, Using Depth, Stereo. Primarily motion based: See also Motion Based Human Detection, Spatio-Temporal Analysis, Pedestrians. Many of the part-based methods: See also Human Detection, People Detection, Pedestrians, Using Body Parts, Body Shape. Tracking issues: See also Tracking People, Human Tracking, Pedestrian Tracking. And: See also Finding Faces in Images, Face Detection.

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IEEE Abstract. 0402
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Combination of Feature Extraction Methods for SVM Pedestrian Detection,
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IEEE DOI 0706
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Cao, X.B.[Xian-Bin], Qiao, H.[Hong], Keane, J.,
A Low-Cost Pedestrian-Detection System With a Single Optical Camera,
ITS(9), No. 1, March 2008, pp. 58-67.
IEEE DOI 0803
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Xu, Y.[Yanwu], Cao, X.B.[Xian-Bin], Qiao, H.[Hong],
An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection,
SMC-B(41), No. 1, February 2011, pp. 107-117.
IEEE DOI 1102
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Chen, Y.T., Chen, C.S.,
Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages,
IP(17), No. 8, August 2008, pp. 1452-1464.
IEEE DOI 0808
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Bellotto, N., Hu, H.S.[Huo-Sheng],
Multisensor-Based Human Detection and Tracking for Mobile Service Robots,
SMC-B(39), No. 1, February 2009, pp. 167-181.
IEEE DOI 0902
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Montabone, S.[Sebastian], Soto, A.[Alvaro],
Human detection using a mobile platform and novel features derived from a visual saliency mechanism,
IVC(28), No. 3, March 2010, pp. 391-402.
Elsevier DOI 1001
Human detection; Visual saliency; Visual features; Moving cameras BibRef

Pszczókowski, S.[Stefan], Soto, A.[Alvaro],
Human Detection in Indoor Environments Using Multiple Visual Cues and a Mobile Robot,
CIARP07(350-359).
Springer DOI 0711
BibRef

Oliveira, L.[Luciano], Nunes, U.[Urbano], Peixoto, P.[Paulo],
On Exploration of Classifier Ensemble Synergism in Pedestrian Detection,
ITS(11), No. 1, March 2010, pp. 16-27.
IEEE DOI 1003
BibRef

Tang, S.P.[Shao-Peng], Goto, S.[Satoshi],
Histogram of Template for Pedestrian Detection,
IEICE(E93-D), No. 7, July 2010, pp. 1737-1744.
WWW Link. 1008
BibRef

Liu, C.[Chang], Wang, G.J.[Gui-Jin], Liu, C.X.[Chun-Xiao], Lin, X.G.[Xing-Gang],
Partial Derivative Guidance for Weak Classifier Mining in Pedestrian Detection,
IEICE(E94-D), No. 8, August 2011, pp. 1721-1724.
WWW Link. 1108
BibRef

Lee, P.H., Lin, Y.L., Chen, S.C., Wu, C.H., Tsai, C.C., Hung, Y.P.,
Viewpoint-Independent Object Detection Based on Two-Dimensional Contours and Three-Dimensional Sizes,
ITS(12), No. 4, December 2011, pp. 1599-1608.
IEEE DOI 1112
Pedestrians and vehicles. BibRef

Li, S., Lu, H.,
Arbitrary Body Segmentation With a Novel Graph Cuts-Based Algorithm,
SPLetters(18), No. 12, December 2011, pp. 753-756.
IEEE DOI 1112
BibRef

Sim, C.H.[Chern-Horng], Rajmadhan, E.[Ekambaram], Ranganath, S.[Surendra],
Detecting people in dense crowds,
MVA(23), No. 2, March 2012, pp. 243-253.
WWW Link. 1202
BibRef

Ye, Q.X.[Qi-Xiang], Liang, J.X.[Ji-Xiang], Jiao, J.B.[Jian-Bin],
Pedestrian Detection in Video Images via Error Correcting Output Code Classification of Manifold Subclasses,
ITS(13), No. 1, March 2012, pp. 193-202.
IEEE DOI 1203
BibRef

Wu, B.[Bo], Liang, J.X.[Ji-Xiang], Ye, Q.X.[Qi-Xiang], Han, Z.J.[Zhen-Jun], Jiao, J.B.[Jian-Bin],
Fast Pedestrian Detection with Laser and Image Data Fusion,
ICIG11(605-608).
IEEE DOI 1109
BibRef

Angermann, M., Robertson, P.,
FootSLAM: Pedestrian Simultaneous Localization and Mapping Without Exteroceptive Sensors: Hitchhiking on Human Perception and Cognition,
PIEEE(100), No. Special Centinnial Issue 2012, pp. 1840-1848.
IEEE DOI 1202
BibRef

Lu, H., Fang, G., Shao, X., Li, X.,
Segmenting Human From Photo Images Based on a Coarse-to-Fine Scheme,
SMC-B(42), No. 3, June 2012, pp. 889-899.
IEEE DOI 1202
BibRef

Gualdi, G.[Giovanni], Prati, A.[Andrea], Cucchiara, R.[Rita],
Multistage Particle Windows for Fast and Accurate Object Detection,
PAMI(34), No. 8, August 2012, pp. 1589-1604.
IEEE DOI 1206
BibRef
Earlier:
A multi-stage pedestrian detection using monolithic classifiers,
AVSBS11(267-272).
IEEE DOI 1111
BibRef
Earlier:
Multi-stage Sampling with Boosting Cascades for Pedestrian Detection in Images and Videos,
ECCV10(VI: 196-209).
Springer DOI 1009
BibRef
And:
Perspective and appearance context for people surveillance in open areas,
UCVP10(13-18).
IEEE DOI 1006
Lower complexity than sliding window search for detection. See also Contextual Information and Covariance Descriptors for People Surveillance: An Application for Safety of Construction Workers. BibRef

Ye, Q., Han, Z., Jiao, J., Liu, J.,
Human Detection in Images via Piecewise Linear Support Vector Machines,
IP(22), No. 2, February 2013, pp. 778-789.
IEEE DOI 1302
BibRef

Fang, X.Y.[Xian-Yong], Zhang, H.[Hu], Zhou, J.[Jian],
Fast window fusion using fuzzy equivalence relation,
PRL(34), No. 6, 15 April 2013, pp. 670-677.
Elsevier DOI 1303
Sliding window; Human detection; Window fusion; Fuzzy equivalence relation BibRef

Nguyen, T.H.B.[Thi-Hai-Binh], Kim, H.[Hakil],
Novel and efficient pedestrian detection using bidirectional PCA,
PR(46), No. 8, August 2013, pp. 2220-2227.
Elsevier DOI 1304
Pedestrian detection; Object detection; Bidirectional PCA BibRef

Serra-Toro, C.[Carlos], Traver, V.J.[V. Javier], Montoliu, R.[Raúl],
Spatial Recurrences for Pedestrian Classification,
JMIV(47), No. 1-2, September 2013, pp. 108-123.
Springer DOI 1307
BibRef
And: A1, A2, Only:
Exploring Relevance Vector Machines for Faster Pedestrian Classification,
IbPRIA13(509-516).
Springer DOI 1307
BibRef
Earlier: A1, A2, Only:
A New Pedestrian Detection Descriptor Based on the Use of Spatial Recurrences,
CAIP11(II: 97-104).
Springer DOI 1109
BibRef

Serra-Toro, C.[Carlos], Hernández-Górriz, Á.[Ángel], Traver, V.J.[V. Javier],
Strategies of Dictionary Usages for Sparse Representations for Pedestrian Classification,
IbPRIA17(96-103).
Springer DOI 1706
BibRef

Tian, H.[Hong], Duan, Z.[Zhu], Abraham, A.[Ajith], Liu, H.B.[Hong-Bo],
A novel multiplex cascade classifier for pedestrian detection,
PRL(34), No. 14, 2013, pp. 1687-1693.
Elsevier DOI 1308
Pedestrian detection BibRef

Negri, P.[Pablo], Goussies, N.[Norberto], Lotito, P.[Pablo],
Detecting pedestrians on a Movement Feature Space,
PR(47), No. 1, 2014, pp. 56-71.
Elsevier DOI 1310
BibRef
Earlier: A1, A3, Only:
Pedestrian Detection Using a Feature Space Based on Colored Level Lines,
CIARP12(885-892).
Springer DOI 1209
Pedestrian detection BibRef

Negri, P.[Pablo],
Pedestrian Detection Using Multi-Objective Optimization,
CIARP15(776-784).
Springer DOI 1511
BibRef

Ma, Y.D.[Ying-Dong], Deng, L.[Liang], Chen, X.K.[Xian-Kai], Guo, N.[Ning],
Integrating Orientation Cue With EOH-OLBP-Based Multilevel Features for Human Detection,
CirSysVideo(23), No. 10, 2013, pp. 1755-1766.
IEEE DOI 1311
cameras BibRef

Boudissa, A.[Ahmed], Tan, J.K.[Joo Kooi], Kim, H.[Hyoungseop], Shinomiya, T.[Takashi], Ishikawa, S.[Seiji],
A Novel Pedestrian Detector on Low-Resolution Images: Gradient LBP Using Patterns of Oriented Edges,
IEICE(E96-D), No. 12, December 2013, pp. 2882-2887.
WWW Link. 1312
BibRef
Earlier: A1, A2, A3, A5, Only:
A simple pedestrian detection using LBP-based patterns of oriented edges,
ICIP12(469-472).
IEEE DOI 1302
BibRef

Yu, J.[Jaehoon], Miyamoto, R., Onoye, T.,
A Speed-Up Scheme Based on Multiple-Instance Pruning for Pedestrian Detection Using a Support Vector Machine,
IP(22), No. 12, 2013, pp. 4752-4761.
IEEE DOI 1312
BibRef
And: Corrections: IP(23), No. 1, January 2014, pp. 478-478.
IEEE DOI 1402
image classification BibRef

Liu, Y.F.[Yun-Fu], Guo, J.M.[Jing-Ming], Chang, C.H.[Che-Hao],
Low resolution pedestrian detection using light robust features and hierarchical system,
PR(47), No. 4, 2014, pp. 1616-1625.
Elsevier DOI 1402
Pedestrian detection BibRef

Golbabaee, M.[Mohammad], Alahi, A.[Alexandre], Vandergheynst, P.[Pierre],
SCOOP: A Real-Time Sparsity Driven People Localization Algorithm,
JMIV(48), No. 1, January 2014, pp. 160-175.
Springer DOI 1402
BibRef

Wang, X.G.[Xiao-Gang], Wang, M.[Meng], Li, W.[Wei],
Scene-Specific Pedestrian Detection for Static Video Surveillance,
PAMI(36), No. 2, February 2014, pp. 361-374.
IEEE DOI 1402
BibRef
Earlier: A2, A3, A1:
Transferring a generic pedestrian detector towards specific scenes,
CVPR12(3274-3281).
IEEE DOI 1208
BibRef
Earlier: A2, A1, Only:
Automatic adaptation of a generic pedestrian detector to a specific traffic scene,
CVPR11(3401-3408).
IEEE DOI 1106
graph theory BibRef

Wang, J.Q.[Jun-Qiu], Yagi, Y.S.[Yasu-Shi],
Shadow extraction and application in pedestrian detection,
JIVP(2014), No. 1, 2014, pp. 12.
DOI Link 1403
See also Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking. BibRef

Vazquez, D.[David], Lopez, A.M.[Antonio M.], Marin, J., Ponsa, D.[Daniel], Geronimo, D.,
Virtual and Real World Adaptationfor Pedestrian Detection,
PAMI(36), No. 4, April 2014, pp. 797-809.
IEEE DOI 1404
BibRef
Earlier: A1, A2, A4, Only:
Unsupervised domain adaptation of virtual and real worlds for pedestrian detection,
ICPR12(3492-3495).
WWW Link. 1302
Accuracy BibRef

Geronimo, D.[David], Lopez, A.M.[Antonio M.],
Vision-based Pedestrian Protection Systems for Intelligent Vehicles,

Springer2014. ISBN 978-1-4614-7986-4.
WWW Link. 1404
BibRef

Zhang, L.Y.[Li-Yan], Kalashnikov, D.V.[Dmitri V.], Mehrotra, S.[Sharad], Vaisenberg, R.[Ronen],
Context-based person identification framework for smart video surveillance,
MVA(25), No. 7, October 2014, pp. 1711-1725.
Springer DOI 1410
BibRef

Shannon, T.[Thomas], Wiltshire, B.[Ben], Spier, E.[Emmet],
Detection of people in military and security context images,
SPIE(Newsroom), September 2, 2014
DOI Link 1410
An autonomous person-detection solution could help alert surveillance operators to potential issues, reducing the cognitive burden and achieving more with less manpower. BibRef

Vineet, V.[Vibhav], Warrell, J.[Jonathan], Torr, P.H.S.[Philip H.S.],
Filter-Based Mean-Field Inference for Random Fields with Higher-Order Terms and Product Label-Spaces,
IJCV(110), No. 1, December 2014, pp. 290-307.
Springer DOI 1411
BibRef
Earlier: ECCV12(V: 31-44).
Springer DOI 1210
BibRef

Vineet, V.[Vibhav], Warrell, J.[Jonathan], Sturgess, P.[Paul], Torr, P.H.S.[Philip H.S.],
Improved Initialization and Gaussian Mixture Pairwise Terms for Dense Random Fields with Mean-field Inference,
BMVC12(73).
DOI Link 1301
BibRef

Vineet, V.[Vibhav], Warrell, J.[Jonathan], Ladicky, L.[Lubor], Torr, P.H.S.[Philip H.S.],
Human Instance Segmentation from Video using Detector-based Conditional Random Fields,
BMVC11(xx-yy).
HTML Version. 1110
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Yang, K.[Kai], Delp, E.J., Du, E.[Eliza],
Categorization-based two-stage pedestrian detection system for naturalistic driving data,
SIViP(8), No. S1, December 2014, pp. 135-144.
WWW Link. 1411
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Li, W.H.[Wen-Hui], Ni, H.Y.[Hong-Yin], Wang, Y.[Ying], Fu, B.[Bo], Liu, P.[Peixun], Wang, S.[Shoujia],
Detection of partially occluded pedestrians by an enhanced cascade detector,
IET-ITS(8), No. 7, 2014, pp. 621-630.
DOI Link 1411
computer vision BibRef

Guo, L.J.[Li-Jun], Cheng, T.T.[Ting-Ting], Xiao, B.[Bo], Zhang, R.[Rong], Zhao, J.Y.[Jie-Yu],
Video human segmentation based on multiple-cue integration,
SP:IC(30), No. 1, 2015, pp. 166-177.
Elsevier DOI 1412
Video segmentation BibRef

Aly, S.,
Partially occluded pedestrian classification using histogram of oriented gradients and local weighted linear kernel support vector machine,
IET-CV(8), No. 6, 2014, pp. 620-628.
DOI Link 1502
computer vision BibRef

Hu, H.M.[Hai-Miao], Zhang, X.W.[Xiao-Wei], Zhang, W.[Wan], Li, B.[Bo],
Joint global-local information pedestrian detection algorithm for outdoor video surveillance,
JVCIR(26), No. 1, 2015, pp. 168-181.
Elsevier DOI 1502
Pedestrian detection BibRef

Tsitsoulis, A.[Athanasios], Bourbakis, N.G.[Nikolaos G.],
A Methodology for Extracting Standing Human Bodies From Single Images,
HMS(45), No. 3, June 2015, pp. 327-338.
IEEE DOI 1506
Estimation BibRef

Du, X.Y.[Xiao-Yun], Laganiere, R.[Robert], Wu, S.[Si],
Enhanced Contour Description for Pedestrian Detection,
IJCVSP(5), No. 1, 2015, pp. 14-22.
PDF File. 1506
Variational LBP combined with HOG. BibRef

Flohr, F.[Fabian], Dumitru-Guzu, M., Kooij, J.F.P., Gavrila, D.M.[Dariu M.],
A Probabilistic Framework for Joint Pedestrian Head and Body Orientation Estimation,
ITS(16), No. 4, August 2015, pp. 1872-1882.
IEEE DOI 1508
BibRef
Earlier: A1, A4, Only:
PedCut: an iterative framework for pedestrian segmentation combining shape models and multiple data cues,
BMVC13(xx-yy).
DOI Link 1402
Detectors BibRef

Enzweiler, M.[Markus], Gavrila, D.M.[Dariu M.],
Integrated pedestrian classification and orientation estimation,
CVPR10(982-989).
IEEE DOI 1006
BibRef
Earlier:
A mixed generative-discriminative framework for pedestrian classification,
CVPR08(1-8).
IEEE DOI 0806
BibRef

de Paulo Carlos, G.[Gérson], Pedrini, H.[Helio], Schwartz, W.R.[William Robson],
Classification schemes based on Partial Least Squares for face identification,
JVCIR(32), No. 1, 2015, pp. 170-179.
Elsevier DOI 1511
BibRef
Earlier:
Fast and Scalable Enrollment for Face Identification Based on Partial Least Squares,
FG13(1-8)
IEEE DOI 1309
Face identification. face recognition BibRef

Schwartz, W.R.[William Robson], Davis, L.S.[Larry S.], Pedrini, H.[Helio],
Local Response Context Applied to Pedestrian Detection,
CIARP11(181-188).
Springer DOI 1111
BibRef

Cunha de Melo, V.H.[Victor Hugo], Leao, S.[Samir], Campos, M.[Mario], Menotti, D.[David], Schwartz, W.R.[William Robson],
Fast pedestrian detection based on a partial least squares cascade,
ICIP13(4146-4150)
IEEE DOI 1402
Pedestrian detection BibRef

Schwartz, W.R.[William R.], Kembhavi, A.[Aniruddha], Harwood, D.[David], Davis, L.S.[Larry S.],
Human Detection Using Partial Least Squares Analysis,
ICCV09(24-31).
IEEE DOI 0909
See also Vehicle Detection Using Partial Least Squares. BibRef

Schwartz, W.R.[William Robson], Gopalan, R.[Raghuraman], Chellappa, R.[Rama], Davis, L.S.[Larry S.],
Robust Human Detection under Occlusion by Integrating Face and Person Detectors,
ICB09(970-979).
Springer DOI 0906
See also Robust and Scalable Approach to Face Identification, A. BibRef

Jordăo, A., de Souza, J.S., Schwartz, W.R.,
A late fusion approach to combine multiple pedestrian detectors,
ICPR16(4250-4255)
IEEE DOI 1705
Computational efficiency, Detectors, Feature extraction, Robots, Surveillance, Windows BibRef

Zhu, C.[Chao], Peng, Y.X.[Yu-Xin],
A Boosted Multi-Task Model for Pedestrian Detection With Occlusion Handling,
IP(24), No. 12, December 2015, pp. 5619-5629.
IEEE DOI 1512
feature extraction BibRef

Kong, K.K.[Kang-Kook], Hong, K.S.[Ki-Sang],
Design of coupled strong classifiers in AdaBoost framework and its application to pedestrian detection,
PRL(68, Part 1), No. 1, 2015, pp. 63-69.
Elsevier DOI 1512
AdaBoost BibRef

Kong, K.K.[Kang-Kook], Lee, J.W.[Jong-Woo], Hong, K.S.[Ki-Sang],
Effective Comparison Features for Pedestrian Detection,
ICIAR16(299-308).
Springer DOI 1608
BibRef

Fusek, R.[Radovan], Sojka, E.[Eduard],
Energy transfer features combined with DCT for object detection,
SIViP(10), No. 3, March 2016, pp. 479-486.
WWW Link. 1602
BibRef
And:
Distance-Based Descriptors and Their Application in the Task of Object Detection,
GCPR14(488-498).
Springer DOI 1411
BibRef

Fusek, R.[Radovan], Sojka, E.[Eduard], Mozdren, K.[Karel], Šurkala, M.[Milan],
An Improvement of Energy-Transfer Features Using DCT for Face Detection,
ICISP14(511-519).
Springer DOI 1406
BibRef
Earlier:
Energy-transfer features and their application in the task of face detection,
AVSS13(147-152)
IEEE DOI 1311
Haar transforms BibRef
And:
Energy-Transfer Features for Pedestrian Detection,
ISVC13(II:425-434).
Springer DOI 1311
BibRef

Vinay, G.K.[G. Krishna], Haque, S.M., Babu, R.V.[R. Venkatesh], Ramakrishnan, K.R.,
Sparse Representation-Based Human Detection: A Scale-Embedded dictionary approach,
SIViP(10), No. 3, March 2016, pp. 585-592.
Springer DOI 1602
BibRef

Liu, Y.F.[Yi-Feng], Zou, L.[Lian], Li, J.[Jie], Yan, J.[Jia], Shi, W.X.[Wen-Xuan], Deng, D.X.[De-Xiang],
Segmentation by weighted aggregation and perceptual hash for pedestrian detection,
JVCIR(36), No. 1, 2016, pp. 80-89.
Elsevier DOI 1603
Pedestrian detection BibRef

Li, Q., Yan, Y., Wang, H.,
Discriminative Weighted Sparse Partial Least Squares for Human Detection,
ITS(17), No. 4, April 2016, pp. 1062-1071.
IEEE DOI 1604
Decision trees BibRef

Said, Y., Atri, M.,
Efficient and high-performance pedestrian detector implementation for intelligent vehicles,
IET-ITS(10), No. 6, 2016, pp. 438-444.
DOI Link 1608
computer vision BibRef

Htike, K.K.[Kyaw Kyaw],
Efficient Labelling of Pedestrian Supervisions,
ELCVIA(15), No. 1, 2016, pp. 77-99.
DOI Link
WWW Link. 1608
BibRef

Shen, J.[Jifeng], Zuo, X.[Xin], Li, J.[Jun], Yang, W.K.[Wan-Kou], Ling, H.B.[Hai-Bin],
A novel pixel neighborhood differential statistic feature for pedestrian and face detection,
PR(63), No. 1, 2017, pp. 127-138.
Elsevier DOI 1612
Pedestrian detection BibRef

Nattoji Rajaram, R., Ohn-Bar, E.[Eshed], Trivedi, M.M.[Mohan M.],
Looking at Pedestrians at Different Scales: A Multiresolution Approach and Evaluations,
ITS(17), No. 12, December 2016, pp. 3565-3576.
IEEE DOI 1612
Computational modeling BibRef

Chen, X., Hwang, J.N., Meng, D., Lee, K.H., de Queiroz, R.L., Yeh, F.M.,
A Quality-of-Content-Based Joint Source and Channel Coding for Human Detections in a Mobile Surveillance Cloud,
CirSysVideo(27), No. 1, January 2017, pp. 19-31.
IEEE DOI 1701
Cameras BibRef

Cai, Y.[Yawei], Tan, X.S.[Xiao-Song], Tan, X.Y.[Xiao-Yang],
Selective Weakly Supervised Human Detection under Arbitrary Poses,
PR(65), No. 1, 2017, pp. 223-237.
Elsevier DOI 1702
Weakly supervised learning BibRef

Li, X., Li, L., Flohr, F., Wang, J., Xiong, H., Bernhard, M., Pan, S., Gavrila, D.M., Li, K.,
A Unified Framework for Concurrent Pedestrian and Cyclist Detection,
ITS(18), No. 2, February 2017, pp. 269-281.
IEEE DOI 1702
Benchmark testing BibRef

Novak, A., Armstrong, N., Caelli, T.M.[Terry M.], Blair, I.,
Bayesian Contrast Measures and Clutter Distribution Determinants of Human Target Detection,
IP(26), No. 3, March 2017, pp. 1115-1126.
IEEE DOI 1703
Bayes methods BibRef

Baek, J.H.[Jeong-Hyun], Kim, J.[Jisu], Kim, E.[Euntai],
Fast and Efficient Pedestrian Detection via the Cascade Implementation of an Additive Kernel Support Vector Machine,
ITS(18), No. 4, April 2017, pp. 902-916.
IEEE DOI 1704
Additives BibRef

Kim, H.K.[Hak-Kyoung], Kim, D.J.[Dai-Jin],
Robust pedestrian detection under deformation using simple boosted features,
IVC(61), No. 1, 2017, pp. 1-11.
Elsevier DOI 1704
Regionlet 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
Pedestrian detection BibRef

Jeong, M., Ko, B.C., Nam, J.Y.,
Early Detection of Sudden Pedestrian Crossing for Safe Driving During Summer Nights,
CirSysVideo(27), No. 6, June 2017, pp. 1368-1380.
IEEE DOI 1706
Cameras, Feature extraction, Finite impulse response filters, Image color analysis, Roads, Support vector machines, Vehicles, Cascade random forest (CaRF), Keimyung University (KMU) pedestrian data set, far-infrared (FIR) image, sudden pedestrian crossing (SPC), virtual reference line BibRef

Li, Q., Wang, H., Yan, Y., Li, B., Chen, C.W.,
Local Co-Occurrence Selection via Partial Least Squares for Pedestrian Detection,
ITS(18), No. 6, June 2017, pp. 1549-1558.
IEEE DOI 1706
Computational efficiency, Decision trees, Detectors, Feature extraction, Intelligent transportation systems, Quantization (signal), Training, Local co-occurrence, partial least squares, pedestrian detection BibRef

Pak, J.M., Ahn, C.K., Shmaliy, Y.S., Shi, P., Lim, M.T.,
Accurate and Reliable Human Localization Using Composite Particle/FIR Filtering,
HMS(47), No. 3, June 2017, pp. 332-342.
IEEE DOI 1706
Atmospheric measurements, Finite impulse response filters, Noise measurement, Particle measurements, Receivers, Robustness, Composite particle/finite impulse response (FIR) filter (CPFF), human localization, particle, filter, (PF) BibRef

Bilal, M.[Muhammad],
Algorithmic optimisation of histogram intersection kernel support vector machine-based pedestrian detection using low complexity features,
IET-CV(11), No. 5, August 2017, pp. 350-357.
DOI Link 1707
BibRef

Suzuki, T., Aoki, Y., Kataoka, H.,
Pedestrian near-miss analysis on vehicle-mounted driving recorders,
MVA17(416-419)
DOI Link 1708
Autonomous vehicles, Benchmark testing, Computer vision, Pattern recognition, Safety, Urban areas, Visualization BibRef

Bak, S.[Slawomir], San-Biagio, M.[Marco], Kumar, R., Murino, V.[Vittorio], Brémond, F.[Francois],
Exploiting Feature Correlations by Brownian Statistics for People Detection and Recognition,
SMCS(47), No. 9, September 2017, pp. 2538-2549.
IEEE DOI 1708
Computer vision, Correlation, Covariance matrices, Feature extraction, Manifolds, Market research, Standards, Brownian descriptor, covariance descriptor, pedestrian detection, reidentification BibRef


Ke, R., Lutin, J., Spears, J., Wang, Y.,
A Cost-Effective Framework for Automated Vehicle-Pedestrian Near-Miss Detection Through Onboard Monocular Vision,
Traffic17(898-905)
IEEE DOI 1709
Cameras, Feature extraction, Safety, Sensors, Surveillance, Tracking, Videos 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, Computer architecture, Fires, Heating, systems BibRef

Eldesokey, A.[Abdelrahman], Felsberg, M.[Michael], Khan, F.S.[Fahad Shahbaz],
Ellipse Detection for Visual Cyclists Analysis 'In the Wild',
CAIP17(I: 319-331).
Springer DOI 1708
BibRef

Chandran, A.K., Subramaniam, A., Wong, W.C., Yang, J., Chaturvedi, K.A.,
A PTZ camera based people-occupancy estimation system (PCBPOES),
MVA17(145-148)
DOI Link 1708
Cameras, Head, Lighting, Magnetic heads, Probabilistic logic, Retina, Support vector machines. BibRef

Wang, H., Gu, Y., Kamijo, S.,
Pedestrian positioning in urban city with the aid of Google maps street view,
MVA17(456-459)
DOI Link 1708
Buildings, Cameras, Google, Image matching, Meters, Urban, areas BibRef

Coniglio, C.[Christophe], Meurie, C.[Cyril], Lézoray, O.[Olivier], Berbineau, M.[Marion],
People silhouette extraction from people detection bounding boxes in images,
PRL(93), No. 1, 2017, pp. 182-191.
Elsevier DOI 1706
People detection BibRef

Kuranuki, Y., Patras, I.,
Minimal filtered channel features for pedestrian detection,
ICPR16(681-686)
IEEE DOI 1705
Color, Computer architecture, Decorrelation, Feature extraction, Optical filters, Shape, Training BibRef

Wang, D.[Dan], Zhang, C.Y.[Chong-Yang], Cheng, H.[Hao], Shang, Y.F.[Yan-Feng], Mei, L.[Lin],
SPID: Surveillance Pedestrian Image Dataset and Performance Evaluation for Pedestrian Detection,
BEST16(III: 463-477).
Springer DOI 1704
Dataset, Pedestrians. BibRef

Zhu, Y.S.[You-Song], Wang, J.Q.[Jin-Qiao], Zhao, C.[Chaoyang], 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

Zhou, C.L.[Chun-Luan], Yuan, J.S.[Jun-Song],
Learning to Integrate Occlusion-Specific Detectors for Heavily Occluded Pedestrian Detection,
ACCV16(II: 305-320).
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

Alzughaibi, A., Chaczko, Z.,
Human detection model using feature extraction method in video frames,
ICVNZ16(1-6)
IEEE DOI 1701
Computational modeling BibRef

Cao, C., Wang, Y.[Yu], Kato, J.[Jien], Zhang, G.[Guanwen], Mase, K.[Kenji],
Solving Occlusion Problem in Pedestrian Detection by Constructing Discriminative Part Layers,
WACV17(91-99)
IEEE DOI 1609
Data mining, Detectors, Feature extraction, Pipelines, Robustness, Training, Visualization BibRef

Kokubo, Y., Wang, Y.[Yu], Kato, J.[Jien], Zhang, G.[Guanwen], Mase, K.[Kenji],
Add-On Strategies for Fine-Grained Pedestrian Classification,
DICTA16(1-6)
IEEE DOI 1701
Feature extraction BibRef

Bowers, J., Green, R.,
Improving pedestrian detection,
ICVNZ16(1-5)
IEEE DOI 1701
Biological neural networks BibRef

Lee, D.[Donghoon], Cha, G.[Geonho], Yang, M.H.[Ming-Hsuan], Oh, S.H.[Song-Hwai],
Individualness and Determinantal Point Processes for Pedestrian Detection,
ECCV16(VI: 330-346).
Springer DOI 1611
BibRef

Boui, M., Hadj-Abdelkader, H.[Hicham], Ababsa, F.E., Bouyakhf, E.H.,
New approach for human detection in spherical images,
ICIP16(604-608)
IEEE DOI 1610
Adaptation models BibRef

Zhang, S., Zhu, Q., Roy-Chowdhury, A.,
Adaptive algorithm selection, with applications in pedestrian detection,
ICIP16(3768-3772)
IEEE DOI 1610
Algorithm design and analysis BibRef

Correia, A.J.L., Schwartz, W.R.,
Oblique random forest based on partial least squares applied to pedestrian detection,
ICIP16(2931-2935)
IEEE DOI 1610
Computer vision BibRef

Errami, M., Rziza, M.[Mohammed],
Improving Pedestrian Detection Using Support Vector Regression,
CGiV16(156-160)
IEEE DOI 1608
Haar transforms BibRef

Rehder, E., Kloeden, H.,
Goal-Directed Pedestrian Prediction,
CVRoads15(139-147)
IEEE DOI 1602
Context BibRef

Toca, C.[Cosmin], Ciuc, M.[Mihai], Patrascu, C.[Carmen],
Normalized Autobinomial Markov Channels For Pedestrian Detection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Yang, Y.[Yi], Wang, Z.H.[Zhen-Hua], Wu, F.C.[Fu-Chao],
Exploring Prior Knowledge for Pedestrian Detection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Xu, P.[Philippe], Davoine, F.[Franck], Denoeux, T.[Thierry],
Evidential combination of pedestrian detectors,
BMVC14(xx-yy).
HTML Version. 1410
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Abid, N.[Nesrine], Loukil, K.[Kais], Ayedi, W.[Walid], Ammari, A.C.[Ahmed Chiheb], Abid, M.[Mohamed],
Optimized Parallel Model of Covariance Based Person Detection,
CIAP15(II:287-298).
Springer DOI 1511
BibRef

Arana-Daniel, N.[Nancy], Cibrian-Decena, I.[Isabel],
Recognition of Non-pedestrian Human Forms Through Locally Weighted Descriptors,
CIARP15(751-759).
Springer DOI 1511
BibRef

Xu, R.[Rong], Ueno, S.[Satoshi], Kobayashi, T.[Tatsuya], Makibuchi, N.[Naoya], Naito, S.[Sei],
Human Area Refinement for Human Detection,
CIAP15(II:130-141).
Springer DOI 1511
BibRef

Ma, Z.[Zheng], Yu, L.[Lei], Chan, A.B.[Antoni B.],
Small instance detection by integer programming on object density maps,
CVPR15(3689-3697)
IEEE DOI 1510
BibRef

Jiang, Y.S.[Yun-Sheng], Ma, J.W.[Jin-Wen],
Combination features and models for human detection,
CVPR15(240-248)
IEEE DOI 1510
BibRef

Zhang, S.S.[Shan-Shan], Benenson, R.[Rodrigo], Schiele, B.[Bernt],
Filtered channel features for pedestrian detection,
CVPR15(1751-1760)
IEEE DOI 1510
BibRef

Hosang, J.[Jan], Omran, M.[Mohamed], Benenson, R.[Rodrigo], Schiele, B.[Bernt],
Taking a deeper look at pedestrians,
CVPR15(4073-4082)
IEEE DOI 1510
BibRef

Becker, S.[Stefan], Kieritz, H.[Hilke], Hübner, W.[Wolfgang], Arens, M.[Michael],
On the Benefit of State Separation for Tracking in Image Space with an Interacting Multiple Model Filter,
ICISP16(3-11).
WWW Link. 1606
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Becker, S.[Stefan], Hubner, W.[Wolfgang], Arens, M.[Michael],
Annotation driven MAP search space estimation for sliding-window based person detection,
MVA15(430-434)
IEEE DOI 1507
Cameras BibRef

Ma, P.[Puhao], Sun, L.[Lei], Ai, H.Z.[Hai-Zhou], Sakai, S.[Shun],
Boosted pedestrian detector adaptation in specific scenes,
MVA15(230-233)
IEEE DOI 1507
Detectors BibRef

Gil, J.I.[Jong-In], Mahmoudpour, S., Kim, M.,
Automatic light control system using fish-eye lens camera,
FCV15(1-3)
IEEE DOI 1506
human detection. object detection BibRef

Jeon, S.P.[Seong Pyo], Lee, Y.S.[Yoon Suk], Choi, K.N.[Kwang Nam],
Movement direction-based approaches for pedestrian detection in road scenes,
FCV15(1-4)
IEEE DOI 1506
driver information systems BibRef

Blondel, P., Potelle, A., Pégard, C., Lozano, R., Lara, D.,
Dynamic collaboration of far-infrared and visible spectrum for human detection,
ICPR16(698-703)
IEEE DOI 1705
BibRef
Earlier: A1, A2, A3, A4, Only:
Fast and viewpoint robust human detection in uncluttered environments,
VCIP14(522-525)
IEEE DOI 1504
Cameras, Collaboration, Detectors, Feature extraction, Optimization, Stereo image processing, Synchronization. BibRef

Shao, S.[Song], Liu, H.[Hong], Wang, X.D.[Xiang-Dong], Qian, Y.L.[Yue-Liang],
Local Associated Features for Pedestrian Detection,
RoLoD14(513-526).
Springer DOI 1504
BibRef

Hwang, S.[Soonmin], Oh, T.H.[Tae-Hyun], Kweon, I.S.[In So],
A Two Phase Approach for Pedestrian Detection,
IVVT14(459-474).
Springer DOI 1504
BibRef

Wang, X.[Xiao], Chen, J.[Jun], Fang, W.H.[Wen-Hua], Liang, C.[Chao], Zhang, C.J.[Chun-Jie], Hu, R.[Ruimin],
Pedestrian detection from salient regions,
ICIP14(2423-2426)
IEEE DOI 1502
Bayes methods BibRef

Zhang, X.G.[Xing-Guo], Chen, G.[Guoyue], Saruta, K.[Kazuki], Terata, Y.[Yuki],
A Simple Visual Words Selection Strategy for Pedestrian Detection,
ISVC14(I: 658-667).
Springer DOI 1501
BibRef

Tani, Y.[Yuta], Hotta, K.[Kazuhiro],
Robust Human Detection to Pose and Occlusion Using Bag-of-Words,
ICPR14(4376-4381)
IEEE DOI 1412
Accuracy BibRef

de Melo, V.H.C.[Victor Hugo Cunha], Leao, S.[Samir], Menotti, D.[David], Schwartz, W.R.[William Robson],
An Optimized Sliding Window Approach to Pedestrian Detection,
ICPR14(4346-4351)
IEEE DOI 1412
Accuracy BibRef

Nilsson, J.[Jonas], Andersson, P.[Patrik], Gu, I.Y.H.[Irene Y.H.], Fredriksson, J.[Jonas],
Pedestrian Detection Using Augmented Training Data,
ICPR14(4548-4553)
IEEE DOI 1412
Data models BibRef

De Smedt, F.[Floris], Puttemans, S., Goedemé, T.[Toon],
How to reach top accuracy for a visual pedestrian warning system from a car?,
IPTA16(1-6)
IEEE DOI 1703
alarm systems BibRef

De Smedt, F.[Floris], van Beeck, K.[Kristof], Tuytelaars, T.[Tinne], Goedeme, T.[Toon],
The Combinator: Optimal Combination of Multiple Pedestrian Detectors,
ICPR14(3522-3527)
IEEE DOI 1412
Accuracy BibRef

Bartoli, F.[Federico], Lisanti, G.[Giuseppe], Karaman, S.[Svebor], Bagdanov, A.D.[Andrew D.], del Bimbo, A.[Alberto],
Unsupervised Scene Adaptation for Faster Multi-scale Pedestrian Detection,
ICPR14(3534-3539)
IEEE DOI 1412
Accuracy BibRef

Frejlichowski, D.[Dariusz], Gosciewska, K.[Katarzyna], Forczmanski, P.[Pawel], Hofman, R.[Radoslaw],
Human Detection for a Video Surveillance Applied in the 'SmartMonitor' System,
ICCVG14(220-227).
Springer DOI 1410
BibRef

Costea, A.D.[Arthur Daniel], Nedevschi, S.[Sergiu],
Semantic Channels for Fast Pedestrian Detection,
CVPR16(2360-2368)
IEEE DOI 1612
BibRef
Earlier:
Word Channel Based Multiscale Pedestrian Detection without Image Resizing and Using Only One Classifier,
CVPR14(2393-2400)
IEEE DOI 1409
boosting BibRef

Sangineto, E.[Enver],
Statistical and Spatial Consensus Collection for Detector Adaptation,
ECCV14(III: 456-471).
Springer DOI 1408
Adaptation of pedestrian detectors toward specific scenarios. BibRef

Bell, A.E.,
Robust feature vector for efficient human detection,
AIPR13(1-5)
IEEE DOI 1408
data compression BibRef

Sager, H.[Hisham], Hoff, W.[William],
Pedestrian detection in low resolution videos,
WACV14(668-673)
IEEE DOI 1406
Detectors BibRef

Tao, J.L.[Jun-Li], Klette, R.,
Part-Based RDF for Direction Classification of Pedestrians, and a Benchmark,
IVVT14(418-432).
Springer DOI 1504
BibRef
Earlier:
Integrated Pedestrian and Direction Classification Using a Random Decision Forest,
AutoDrive13(230-237)
IEEE DOI 1403
behavioural sciences computing BibRef

Li, Y.R.[Yan-Ran], Yu, S.Q.[Shi-Qi], Wu, S.Y.[Sheng-Yin],
Framelet features for pedestrian detection in noisy depth images,
ICIP13(2949-2952)
IEEE DOI 1402
Pedestrian detection;adaptive selection features;framelet BibRef

Li, Z.X.[Zhi-Xuan], Zhao, Y.Y.[Yan-Yun],
Pedestrian detection in single frame by edgelet-LBP part detectors,
AVSS13(420-425)
IEEE DOI 1311
edge detection BibRef

Mekonnen, A.A.[Alhayat Ali], Lerasle, F.[Frédéric], Herbulot, A.[Ariane], Briand, C.,
People Detection with Heterogeneous Features and Explicit Optimization on Computation Time,
ICPR14(4322-4327)
IEEE DOI 1412
BibRef
Earlier: A1, A2, A3, Only:
Person Detection with a Computation Time Weighted AdaBoost,
ACIVS13(632-644).
Springer DOI 1311
Cascading style sheets BibRef

Rujikietgumjorn, S.[Sitapa], Collins, R.T.[Robert T.],
Optimized Pedestrian Detection for Multiple and Occluded People,
CVPR13(3690-3697)
IEEE DOI 1309
BibRef

Hosang, J.[Jan], Benenson, R.[Rodrigo], Schiele, B.[Bernt],
How good are detection proposals, really?,
BMVC14(xx-yy).
HTML Version. 1410
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Benenson, R.[Rodrigo], Mathias, M.[Markus], Tuytelaars, T.[Tinne], Van Gool, L.J.[Luc J.],
Seeking the Strongest Rigid Detector,
CVPR13(3666-3673)
IEEE DOI 1309
objects detection; pedestrian detection BibRef

Benenson, R.[Rodrigo], Mathias, M.[Markus], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
Fast Stixel Computation for Fast Pedestrian Detection,
CVVT12(III: 11-20).
Springer DOI 1210
BibRef
And:
Pedestrian detection at 100 frames per second,
CVPR12(2903-2910).
IEEE DOI 1208
BibRef

Taiana, M.[Matteo], Nascimento, J.C.[Jacinto C.], Bernardino, A.[Alexandre],
An Improved Labelling for the INRIA Person Data Set for Pedestrian Detection,
IbPRIA13(286-295).
Springer DOI 1307
BibRef

Wang, J.Q.[Jian-Qing], Wang, M.[Min], Qiao, H.[Hong], Keane, J.,
Oriented Gradient Context for pedestrian detection,
ICARCV12(1142-1147).
IEEE DOI 1304
BibRef

Wang, L.[Li], Chan, K.L.[Kap Luk], Wang, G.[Gang],
Human Detection with Occlusion Handling by Over-Segmentation and Clustering on Foreground Regions,
CDF12(II:197-208).
Springer DOI 1304
BibRef

Hao, P.Y.[Peng-Yi], Kamata, S.I.[Sei-Ichiro],
An efficient video retrieval scheme based on facial signatures,
ICIP13(2699-2703)
IEEE DOI 1402
BibRef
Earlier:
Unsupervised people organization and its application on individual retrieval from videos,
ICPR12(2001-2004).
WWW Link. 1302
Linear discriminant analysis;Signature;Video retrieval BibRef

Ahmed, I.[Imran], Carter, J.N.[John N.],
A robust person detector for overhead views,
ICPR12(1483-1486).
WWW Link. 1302
BibRef

Wang, Q.Y.[Qing-Yuan], Pang, J.B.[Jun-Biao], Liu, G.[Guoyi], Qin, L.[Lei], Huang, Q.M.[Qing-Ming], Jiang, S.Q.[Shu-Qiang],
Color Maximal-Dissimilarity Pattern for pedestrian detection,
ICPR12(1952-1955).
WWW Link. 1302
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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
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Tasson, D., Montagnini, A., Marzotto, R., Farenzena, M., Cristani, M.,
FPGA-based pedestrian detection under strong distortions,
ECVW15(65-70)
IEEE DOI 1510
Cameras BibRef

Martelli, S.[Samuele], Tosato, D.[Diego], Cristani, M.[Marco], Murino, V.[Vittorio],
Fast FPGA-based architecture for pedestrian detection based on covariance matrices,
ICIP11(389-392).
IEEE DOI 1201
BibRef

Nodari, A.[Angelo], Vanetti, M.[Marco], Gallo, I.[Ignazio],
Digital privacy: Replacing pedestrians from Google Street View images,
ICPR12(2889-2893).
WWW Link. 1302
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Koyama, T.[Tatsuya], Nakashima, Y.[Yuta], Babaguchi, N.[Noboru],
Markov random field-based real-time detection of intentionally-captured persons,
ICIP12(1377-1380).
IEEE DOI 1302
BibRef

Garcia-Martin, A.[Alvaro], Cavallaro, A.[Andrea], Martinez, J.M.[Jose M.],
People-background segmentation with unequal error cost,
ICIP12(157-160).
IEEE DOI 1302
BibRef

Cao, Y.Y.[Yun-Yun], Pranata, S.[Sugiri], Yasugi, M.[Makoto], Niu, Z.H.[Zhi-Heng], Nishimura, H.[Hirofumi],
Stagged multi-scale LBP for pedestrian detection,
ICIP12(449-452).
IEEE DOI 1302
BibRef

Liu, Z.F.[Zhi-Fang], Duan, G.Q.[Gen-Quan], Ai, H.Z.[Hai-Zhou], Yamashita, T.[Takayoshi],
Adaptation of boosted pedestrian detectors by feature reselection,
ICIP12(481-484).
IEEE DOI 1302
BibRef

Huang, P.J.[Po-Jui], Chen, D.Y.[Duan-Yu],
Robust wheelchair pedestrian detection using sparse representation,
VCIP12(1-5).
IEEE DOI 1302
BibRef

Tang, D.H.[Dan-Hang], Liu, Y.[Yang], Kim, T.K.[Tae-Kyun],
Fast Pedestrian Detection by Cascaded Random Forest with Dominant Orientation Templates,
BMVC12(58).
DOI Link 1301
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Ladický, L.[Lubor], Torr, P.H.S.[Philip H.S.], Zisserman, A.[Andrew],
Latent SVMs for Human Detection with a Locally Affine Deformation Field,
BMVC12(10).
DOI Link 1301
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Evans, M.[Murray], Li, L.Z.[Long-Zhen], Ferryman, J.M.[James M.],
Suppression of Detection Ghosts in Homography Based Pedestrian Detection,
AVSS12(31-36).
IEEE DOI 1211
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Kamberov, G.[George], Burlick, M.[Matt], Karydas, L.[Lazaros], Koteoglou, O.[Olga],
Scar: Dynamic Adaptation for Person Detection and Persistence Analysis in Unconstrained Videos,
ISVC12(II: 176-187).
Springer DOI 1209
BibRef

Ding, Y.Y.[Yuan-Yuan], Xiao, J.[Jing],
Contextual boost for pedestrian detection,
CVPR12(2895-2902).
IEEE DOI 1208
BibRef

Han, H.[Hong], Fan, Y.J.[You-Jian], Jiao, L.C.[Li-Cheng], Chen, Z.C.[Zhi-Chao],
Concatenated edge and co-occurrence feature extracted from Curvelet Transform for human detection,
IVCNZ10(1-8).
IEEE DOI 1203
BibRef

Munaro, M.[Matteo], Cenedese, A.[Angelo],
Scene specific people detection by simple human interaction,
HICV11(1250-1255).
IEEE DOI 1201
BibRef

Zini, L.[Luca], Odone, F.[Francesca],
Efficient pedestrian detection with group lasso,
VS11(1777-1784).
IEEE DOI 1201
BibRef

Nguyen, D.T.[Duc Thanh],
A Novel Chamfer Template Matching Method Using Variational Mean Field,
CVPR14(2425-2432)
IEEE DOI 1409
Chamfer template matching; object detection; variational mean field BibRef

Nguyen, D.T.[Duc Thanh], Ogunbona, P.[Philip], Li, W.Q.[Wan-Qing],
Detecting humans under occlusion using variational mean field method,
ICIP11(2049-2052).
IEEE DOI 1201
BibRef

Migniot, C.[Cyrille], Bertolino, P.[Pascal], Chassery, J.M.[Jean-Marc],
Automatic people segmentation with a template-driven graph cut,
ICIP11(3149-3152).
IEEE DOI 1201
BibRef

Wu, J.C.[Jin-Chen], Chen, W.[Wei], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Partial Least Squares based subwindow search for pedestrian detection,
ICIP11(3565-3568).
IEEE DOI 1201
BibRef

Cao, Y.Y.[Yun-Yun], Pranata, S.[Sugiri], Nishimura, H.[Hirofumi],
Local Binary Pattern features for pedestrian detection at night/dark environment,
ICIP11(2053-2056).
IEEE DOI 1201
BibRef

Chen, X.T.[Xiao-Tang], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Direction-based stochastic matching for pedestrian recognition in non-overlapping cameras,
ICIP11(2065-2068).
IEEE DOI 1201
BibRef

Wang, J.Q.[Jun-Qiang], Ma, H.D.[Hua-Dong],
MPL-Boosted Integrable Features Pool for pedestrian detection,
ICIP11(805-808).
IEEE DOI 1201
BibRef

Ma, Y.D.[Ying-Dong], Chen, X.[Xiankai], Jin, L.[Liu], Chen, G.[George],
A Monocular Human Detection System Based on EOH and Oriented LBP Features,
ISVC11(I: 551-562).
Springer DOI 1109
BibRef

El Guebaly, T.[Tarek], Bouguila, N.[Nizar],
A nonparametric Bayesian approach for enhanced pedestrian detection and foreground segmentation,
OTCBVS11(21-26).
IEEE DOI 1106
BibRef

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

Kim, D.H.[Dae-Hwan], Kim, Y.[Yeonho], Kim, D.J.[Dai-Jin],
Separating Occluded Humans by Bayesian Pixel Classifier with Re-weighted Posterior Probability,
ACIVS11(543-553).
Springer DOI 1108
BibRef

Pedrocca, P.J.[Pablo Julian], Allili, M.S.[Mohand Saďd],
Real-Time People Detection in Videos Using Geometrical Features and Adaptive Boosting,
ICIAR11(I: 314-324).
Springer DOI 1106
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

Zheng, Y.B.[Yong-Bin], Shen, C.H.[Chun-Hua], Hartley, R.I.[Richard I.], Huang, X.S.[Xin-Sheng],
Pyramid Center-Symmetric Local Binary/Trinary Patterns for Effective Pedestrian Detection,
ACCV10(IV: 281-292).
Springer DOI 1011
BibRef

Leithy, A.[Alaa], Moustafa, M.N.[Mohamed N.], Wahba, A.[Ayman],
Fast and Accurate Pedestrian Detection Using a Cascade of Multiple Features,
VS10(153-163).
Springer DOI 1109
BibRef
And:
Cascade of Complementary Features for Fast and Accurate Pedestrian Detection,
PSIVT10(343-348).
IEEE DOI 1011
BibRef

Barnich, O.[Olivier], Piérard, S.[Sébastien], van Droogenbroeck, M.[Marc],
A Virtual Curtain for the Detection of Humans and Access Control,
ACIVS10(II: 98-109).
Springer DOI 1012
BibRef

Yu, J.[Jie], Farin, D.[Dirk], Kruger, C.[Christof], Schiele, B.[Bernt],
Improving person detection using synthetic training data,
ICIP10(3477-3480).
IEEE DOI 1009
BibRef

Middleton, L.[Lee], Snowdon, J.R.[James R.],
Histogram of confidences for person detection,
ICIP10(1841-1844).
IEEE DOI 1009
BibRef

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

Garcia-Martin, A., Martinez, J.M.,
Robust Real Time Moving People Detection in Surveillance Scenarios,
AVSS10(241-247).
IEEE DOI 1009
BibRef

Shen, J.[Jiali], Yan, W.Q.[Wei-Qi], Miller, P., Zhou, H.Y.[Hui-Yu],
Human Localization in a Cluttered Space Using Multiple Cameras,
AVSS10(85-90).
IEEE DOI 1009
BibRef

Atienza-Vanacloig, V.[Vicente], Rosell-Ortega, J.[Juan], Andreu-Garcia, G.[Gabriela], Valiente-Gonalez, J.M.[Jose Miguel],
Locating People in Images by Optimal Cue Integration,
ICPR10(1804-1807).
IEEE DOI 1008
BibRef

Heimonen, T.A.[Teuvo Antero], Heikkila, J.[Janne],
A Human Detection Framework for Heavy Machinery,
ICPR10(416-419).
IEEE DOI 1008
BibRef

Ma, W.H.[Wen-Hua], He, P.[Peng], Huang, L.[Lei], Liu, C.P.[Chang-Ping],
Context Inspired Pedestrian Detection in Far-Field Videos,
ICPR10(3009-3012).
IEEE DOI 1008
BibRef

Hong, X.P.[Xiao-Peng], Chang, H.[Hong], Chen, X.L.[Xi-Lin], Gao, W.[Wen],
Boosted Sigma Set for Pedestrian Detection,
ICPR10(3017-3020).
IEEE DOI 1008
See also Sigma Set: A small second order statistical region descriptor. BibRef

Cai, Y.H.[Ying-Hao], Takala, V.[Valtteri], Pietikainen, M.[Matti],
Matching Groups of People by Covariance Descriptor,
ICPR10(2744-2747).
IEEE DOI 1008
BibRef

Simonnet, D.[Damien], Velastin, S.A.[Sergio A.],
Pedestrian detection based on adaboost algorithm with a pseudo-calibrated camera,
IPTA10(54-59).
IEEE DOI 1007
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Flores, A.[Arturo], Belongie, S.J.[Serge J.],
Removing pedestrians from Google street view images,
IWMV10(53-58).
IEEE DOI 1006
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Ott, P.[Patrick], Everingham, M.[Mark],
Implicit color segmentation features for pedestrian and object detection,
ICCV09(723-730).
IEEE DOI 0909
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Pang, J.B.[Jun-Biao], Huang, Q.M.[Qing-Ming], Jiang, S.Q.[Shu-Qiang], Wu, Z.P.[Zhi-Peng],
Transfer pedestrian detector towards view-adaptiveness and efficiency,
ObjectEvent09(609-616).
IEEE DOI 0910
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Hou, Y.L.[Ya-Li], Pang, G.K.H.[Grantham K.H.],
Human detection in crowded scenes,
ICIP10(721-724).
IEEE DOI 1009
BibRef
Earlier:
Human detection in a challenging situation,
ICIP09(2561-2564).
IEEE DOI 0911
BibRef

Liao, C.T.[Chia-Te], Lai, S.H.[Shang-Hong], Wang, W.H.[Wen-Hao],
A hierarchical image kernel with application to pedestrian identification for video surveillance,
ICIP09(1125-1128).
IEEE DOI 0911
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Lee, J.T.[Jong Taek], Chen, C.C.[Chia-Chih], Aggarwal, J.K.,
Recognizing human-vehicle interactions from aerial video without training,
WAVP11(53-60).
IEEE DOI 1106
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Xia, L.[Lu], Chen, C.C.[Chia-Chih], Aggarwal, J.K.,
View invariant human action recognition using histograms of 3D joints,
HAU3D12(20-27).
IEEE DOI 1207
BibRef
And:
Human detection using depth information by Kinect,
HAU3D11(15-22).
IEEE DOI 1106
BibRef

Ryoo, M.S., Lee, J.T.[Jong Taek], Aggarwal, J.K.,
Video scene analysis of interactions between humans and vehicles using event context,
CIVR10(462-469).
DOI Link 1007
BibRef
Earlier: A2, A1, A3:
View independent recognition of human-vehicle interactions using 3-D models,
WMVC09(1-8).
IEEE DOI 0912
BibRef

Yu, X.G.[Xin-Guo], Dong, L.[Li], Li, L.Y.[Li-Yuan], Hoe, J.K.E.[Jerry Kah Eng],
Lift-button detection and recognition for service robot in buildings,
ICIP09(313-316).
IEEE DOI 0911
BibRef

Li, L.Y.[Li-Yuan], Hoe, J.K.E.[Jerry Kah Eng], Yan, S.C.[Shui-Cheng], Yu, X.G.[Xin-Guo],
ML-fusion based multi-model human detection and tracking for robust human-robot interfaces,
WACV09(1-8).
IEEE DOI 0912
BibRef

Bolme, D.S.[David S.], Beveridge, J.R.[J. Ross], Draper, B.A.[Bruce A.], Lui, Y.M.[Yui Man],
Visual object tracking using adaptive correlation filters,
CVPR10(2544-2550).
IEEE DOI 1006
BibRef
Earlier: A1, A4, A3, A2:
Simple real-time human detection using a single correlation filter,
PETS-Winter09(1-8).
IEEE DOI 0912
BibRef

Lai, J., Ford, J.J., O'Shea, P., Walker, R.,
Hidden Markov Model Filter Banks for Dim Target Detection from Image Sequences,
DICTA08(312-319).
IEEE DOI 0812
BibRef

Yu, L.P.[Li-Ping], Yao, W.[Wentao],
Pedestrian Detection Fusion Method Based on Mean Shift,
ICMV09(204-207).
IEEE DOI 0912
BibRef

Brits, A.M.[Alessio M.], Tapamo, J.R.[Jules R.],
A Shape and Energy Based Approach to Vertical People Separation in Video Surveillance,
ISVC09(II: 345-356).
Springer DOI 0911
BibRef

Rapus, M.[Martin], Munder, S.[Stefan], Baratoff, G.[Gregory], Denzler, J.[Joachim],
Pedestrian Detection by Probabilistic Component Assembly,
DAGM09(91-100).
Springer DOI 0909
BibRef

Pang, J.B.[Jun-Biao], Huang, Q.M.[Qing-Ming], Jiang, S.Q.[Shu-Qiang],
Multiple Instance Boost Using Graph Embedding Based Decision Stump for Pedestrian Detection,
ECCV08(IV: 541-552).
Springer DOI 0810
BibRef

Lu, H.C.[Hu-Chuan], Jia, C.H.[Chun-Hua], Zhang, R.J.[Rui-Juan],
An effective method for detection and segmentation of the body of human in the view of a single stationary camera,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Thome, N.[Nicolas], Ambellouis, S.[Sebastien],
A bottom-up, view-point invariant human detector,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Meng, L.[Long], Li, L.[Liang], Mei, S.[Shuqi], Wu, W.G.[Wei-Guo],
Directional entropy feature for human detection,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Jones, M.J.[Michael J.], Snow, D.[Daniel],
Pedestrian detection using boosted features over many frames,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Park, J.M.[Jung-Me], Luo, Y.[Yun], Wang, H.X.[Hao-Xing], Murphey, Y.L.[Yi L.],
Pedestrian detection by modeling local convex shape features,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Leyrit, L.[Laetitia], Chateau, T.[Thierry], Tournayre, C.[Christophe], Lapreste, J.T.[Jean-Thierry],
Visual pedestrian recognition in weak classifier space using nonlinear parametric models,
ICIP08(2392-2395).
IEEE DOI 0810
BibRef

Abramson, Y., Steux, B.,
Hardware-friendly pedestrian detection and impact prediction,
IVS04(590-595).
WWW Link. 0411
BibRef

Colombo, A.[Alberto], Orwell, J.[James], Velastin, S.A.[Sergio A.],
Colour Constancy Techniques for Re-Recognition of Pedestrians from Multiple Surveillance Cameras,
M2SFA208(xx-yy). 0810
BibRef

Zhang, C.[Cha], Hamid, R.[Raffay], Zhang, Z.Y.[Zheng-You],
Taylor expansion based classifier adaptation: Application to person detection,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Begard, J., Allezard, N., Sayd, P.,
Real-time human detection in urban scenes: Local descriptors and classifiers selection with AdaBoost-like algorithms,
OTCBVS08(1-8).
IEEE DOI 0806
BibRef
Earlier:
Real-Time Humans Detection in Urban Scenes,
BMVC07(xx-yy).
PDF File. 0709
BibRef

Duan, G.Q.[Gen-Quan], Ai, H.Z.[Hai-Zhou], Lao, S.H.[Shi-Hong],
Human Detection in Video over Large Viewpoint Changes,
ACCV10(II: 683-696).
Springer DOI 1011
BibRef
And:
A Structural Filter Approach to Human Detection,
ECCV10(VI: 238-251).
Springer DOI 1009
BibRef

Duan, G.Q.[Gen-Quan], Huang, C.[Chang], Ai, H.Z.[Hai-Zhou], Lao, S.H.[Shi-Hong],
Boosting Associated Pairing Comparison Features for pedestrian detection,
VS09(1097-1104).
IEEE DOI 0910
See also High-Performance Rotation Invariant Multiview Face Detection. BibRef

Gao, W.[Wei], Ai, H.Z.[Hai-Zhou], Lao, S.H.[Shi-Hong],
Adaptive Contour Features in oriented granular space for human detection and segmentation,
CVPR09(1786-1793).
IEEE DOI 0906
BibRef

Hou, C.[Cong], Ai, H.Z.[Hai-Zhou], Lao, S.H.[Shi-Hong],
Multiview Pedestrian Detection Based on Vector Boosting,
ACCV07(I: 210-219).
Springer DOI 0711
See also High-Performance Rotation Invariant Multiview Face Detection. BibRef

Chen, Y.T.[Yu-Ting], Chen, C.S.[Chu-Song],
A Cascade of Feed-Forward Classifiers for Fast Pedestrian Detection,
ACCV07(I: 905-914).
Springer DOI 0711
BibRef

Schulz, W.[Wolfgang], Enzweiler, M.[Markus], Ehlgen, T.[Tobias],
Pedestrian Recognition from a Moving Catadioptric Camera,
DAGM07(456-465).
Springer DOI 0709
BibRef

Shet, V.D.[Vinay D.], Neumann, J.[Jan], Ramesh, V.[Visvanathan], Davis, L.S.[Larry S.],
Bilattice-based Logical Reasoning for Human Detection,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Gallagher, A.C.[Andrew C.], Blose, A.C.[Andrew C.], Chen, T.H.[Tsu-Han],
Jointly Estimating Demographics and Height with a Calibrated Camera,
ICCV09(1187-1194).
IEEE DOI 0909
See also ground truth based vanishing point detection algorithm, A. BibRef

Gallagher, A.C.[Andrew C.], Chen, T.H.[Tsu-Han],
Understanding images of groups of people,
CVPR09(256-263).
IEEE DOI 0906
BibRef

Gallagher, A.C.[Andrew C.], Chen, T.H.[Tsu-Han],
Using a Markov Network to Recognize People in Consumer Images,
ICIP07(IV: 489-492).
IEEE DOI 0709
BibRef
And:
Using Group Prior to Identify People in Consumer Images,
SLAM07(1-8).
IEEE DOI 0706
BibRef

Feris, R.S.[Rogerio S.], Tian, Y.L.[Ying-Li], Hampapur, A.[Arun],
Capturing People in Surveillance Video,
VS07(1-8).
IEEE DOI 0706
BibRef

Parikh, D.[Devi], Zitnick, C.L.[C. Lawrence],
Finding the weakest link in person detectors,
CVPR11(1425-1432).
IEEE DOI 1106
BibRef

Sivic, J., Zitnick, C.L., Szeliski, R.S.[Richard S.],
Finding people in repeated shots of the same scene,
BMVC06(III:909).
PDF File. 0609
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Davis, L.S.[Larry S.],
Segmenting people in small groups,
VSSN06(1-2).
WWW Link. 0701
BibRef

Harasse, S.[Sebastien], Bonnaud, L.[Laurent], Desvignes, M.[Michel],
A Human Model for Detecting People in Video from Low Level Features,
ICIP06(1845-1848).
IEEE DOI 0610
BibRef
Earlier:
Human model for people detection in dynamic scenes,
ICPR06(I: 335-354).
IEEE DOI 0609
BibRef

Scotti, G., Cuocolo, A., Coelho, C., Marchesotti, L.,
A Novel Pedestrian Classification Algorithm for a High Definition Dual Camera 360 Degrees Surveillance System,
ICIP05(III: 880-883).
IEEE DOI 0512
BibRef

Mori, G.[Greg],
Guiding Model Search Using Segmentation,
ICCV05(II: 1417-1423).
IEEE DOI 0510
Segmentation into "suprepixels" (small regions). Human model composed of sets of superpixels, joints in center of one. BibRef

Zhao, L.[Liang], Davis, L.S.[Larry S.],
Closely Coupled Object Detection and Segmentation,
ICCV05(I: 454-461).
IEEE DOI 0510
BibRef
And:
Segmentation and Appearance Model Building from an Image Sequence,
ICIP05(I: 321-324).
IEEE DOI 0512
Link detection and segmentation, not separate tasks. BibRef

Castillo, C.[Carlos], Chang, C.[Carolina],
An Approach to Vision-Based Person Detection in Robotic Applications,
IbPRIA05(I:209).
Springer DOI 0509
BibRef

Liu, Z.Y.[Zong-Yi], Sarkar, S.[Sudeep],
Challenges in Segmentation of Human Forms in Outdoor Video,
PercOrg04(43).
IEEE DOI 0502
BibRef

Owechko, Y., Medasani, S.,
A Swarm-Based Volition/Attention Framework for Object Recognition,
AttenPerf05(III: 91-91).
IEEE DOI 0507
BibRef

Lombardi, P., Zavidovique, B.,
A context-dependent vision system for pedestrian detection,
IVS04(578-583).
WWW Link. 0411
BibRef
And:
Architectural design issues for bayesian contextual vision,
ICPR04(I: 753-756).
IEEE DOI 0409
BibRef

Dante, A., Brookes, M., Constantinides, A.G.,
Robust multi-body segmentation,
BMVC03(xx-yy).
HTML Version. 0409
BibRef

Ramoser, H., Schlogl, T., Beleznai, C., Winter, M., Bischof, H.,
Shape-based detection of humans for video surveillance applications,
ICIP03(III: 1013-1016).
IEEE DOI 0312
BibRef

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

Lefee, D., Mousset, S., Bertozzi, M., Bensrhair, A.,
Cooperation of passive vision systems in detection and tracking of pedestrians,
IVS04(768-773).
WWW Link. 0411
See also Vehicle Detection by Means of Stereo Vision-Based Obstacles Features Extraction and Monocular Pattern Analysis. BibRef

Sprague, N., Luo, J.B.[Jie-Bo],
Clothed people detection in still images,
ICPR02(III: 585-589).
IEEE DOI 0211
BibRef

Vendrig, J.[Jeroen], Worring, M.[Marcel],
Multimodal Person Identification in Movies,
CIVR02(175-185).
Springer DOI 0208
BibRef

Utsumi, A., Tetsutani, N.,
Human detection using geometrical pixel value structures,
AFGR02(34-39).
IEEE DOI 0206
BibRef

Ozer, I.B., Wolf, W.,
Real-time posture and activity recognition,
Motion02(133-138).
IEEE DOI 0303
BibRef
Earlier:
Human Detection in Compressed Domain,
ICIP01(III: 274-277).
IEEE DOI 0108
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

Pujol, A., Lumbreras, F., Varona, X., Villanueva, J.J.[Juan J.],
Locating People in Indoor Scenes for Real Applications,
ICPR00(Vol IV: 632-635).
IEEE DOI 0009
BibRef

Lee, M.S.[Mi-Suen],
Detecting People in Cluttered Indoor Scenes,
CVPR00(I: 804-809).
IEEE DOI 0005
BibRef

Faulhaber, D., Niemann, H., Weierich, P.,
Detection of Crowds of People by Use of Wavelet Features and Parameter Free Statistical Models,
MVA98(xx-yy). BibRef 9800

Steffens, J.B.[Johannes Bernhard], Elagin, E.V.[Egor Valerievich], Neven, H.[Hartmut],
PersonSpotter: Fast and Robust System for Human Detection, Tracking and Recognition,
AFGR98(516-521).
IEEE DOI BibRef 9800

Kuno, Y., Watanabe, T., Shimosakoda, Y., Nakagawa, S.,
Automated Detection of Human for Visual Surveillance System,
ICPR96(III: 865-869).
IEEE DOI 9608
(Kanasi Laboratory, J) BibRef

Kosugi, M.[Makoto], Yamashita, K.[Kouji],
Person identification system based on a trapezoid pyramid architecture of a gray-level image,
CIAP97(II: 501-508).
Springer DOI 9709
BibRef

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

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Learning, Neural Nets for Human Detection, People Detection, Pedestrians .


Last update:Sep 18, 2017 at 11:34:11