16.7.4.2 Human Detection, People Detection, Pedestrians, Locating

VIPeR: Viewpoint Invariant Pedestrian Recognition,
Pedestrian dataset. 2007. WWW Link.
Dataset, Pedestrians.

Yamasaki, M.[Masafumi],
Moving body detection device of camera,
US_Patent5,627,586, May 6, 1997
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Seki, S.[Susumu], Nagaya, S.[Shigeki], Oka, R.[Ryuichi],
Image recognition method,
US_Patent5,838,839, Nov 17, 1998
WWW Link. template extraction of people BibRef 9811

Ahmad, S.[Subutai], Hunter, K.L.[Kevin L.],
Computer vision system for subject characterization,
US_Patent6,031,934, Feb 29, 2000
WWW Link. BibRef 0002

Curio, C., Edelbrunner, J., Kalinke, T., Tzomakas, C., von Seelen, W.,
Walking pedestrian recognition,
ITS(1), No. 3, September 2000, pp. 155-163.
IEEE Abstract. 0402
BibRef

Wöhler, C.[Christian],
Autonomous in situ training of classification modules in real-time vision systems and its application to pedestrian recognition,
PRL(23), No. 11, September 2002, pp. 1263-1270.
Elsevier DOI 0206
BibRef

Hasegawa, O.[Osamu], Kanade, T.[Takeo],
Type classification, color estimation, and specific target detection of moving targets on public streets,
MVA(16), No. 2, February 2005, pp. 116-121.
Springer DOI 0501
BibRef

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
BibRef

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
BibRef

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
BibRef

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
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

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

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

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

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

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
BibRef

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
BibRef

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

Zhang, X.W.[Xiao-Wei], Li, B.[Bo], Hu, H.M.[Hai-Miao],
Scale-aware hierarchical loss: A multipath RPN for multi-scale pedestrian detection,
VCIP17(1-4)
IEEE DOI 1804
convolution, feature extraction, graph theory, learning (artificial intelligence), minimisation, Scale-aware Weighting 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

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
Earlier: A1, A3, A2:
Video Compression and Retrieval of Moving Object Location Applied to Surveillance,
ICIAR09(906-916).
Springer DOI 0907
Storing moving object information for later surveillance analysis. 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

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

Jordão, 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

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

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

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., Khan, A., Karim Khan, M.U., Kyung, C.M.,
A Low-Complexity Pedestrian Detection Framework for Smart Video Surveillance Systems,
CirSysVideo(27), No. 10, October 2017, pp. 2260-2273.
IEEE DOI 1710
object detection, pedestrians, support vector machines, video surveillance, histogram of oriented gradients, linear support vector machine, 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

Osayamwen, F.[Festus], Tapamo, J.R.[Jules-Raymond],
Improved eigenspectrum regularisation for human activity recognition,
IJCVR(8), No. 4, 2018, pp. 435-454.
DOI Link 1808
BibRef

Toudjeu, I.T.[Ignace Tchangou], Tapamo, J.R.[Jules Raymond],
Slope Pattern Spectra for Human Action Recognition,
ICIAR18(381-389).
Springer DOI 1807
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

Hattori, H.[Hironori], Lee, N.[Namhoon], Boddeti, V.N.[Vishnu Naresh], Beainy, F.[Fares], Kitani, K.M.[Kris M.], Kanade, T.[Takeo],
Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,
IJCV(126), No. 9, September 2018, pp. 1027-1044.
Springer DOI 1809
BibRef

Bartoli, F.[Federico], Lisanti, G.[Giuseppe], Karaman, S.[Svebor], del Bimbo, A.[Alberto],
Scene-dependent proposals for efficient person detection,
PR(87), 2019, pp. 170-178.
Elsevier DOI 1812
Person detection, Scene-dependent proposals, Gaussian mixture model, Scene modelling BibRef

Kooij, J.F.P.[Julian F.P.], Flohr, F.[Fabian], Pool, E.A.I.[Ewoud A.I.], Gavrila, D.M.[Dariu M.],
Context-Based Path Prediction for Targets with Switching Dynamics,
IJCV(127), No. 3, March 2019, pp. 239-262.
Springer DOI 1903
Objects have multiple dynamic modes. BibRef

Yao, L.[Li], Wang, B.F.[Bo-Fan],
Pedestrian detection framework based on magnetic regional regression,
IET-IPR(13), No. 9, 18 July 2019, pp. 1431-1436.
DOI Link 1907
BibRef

Galiyawala, H.[Hiren], Raval, M.S.[Mehul S.], Dave, S.[Shivansh],
Visual appearance based person retrieval in unconstrained environment videos,
IVC(92), 2019, pp. 103816.
Elsevier DOI 1912
Linear filtering, Person retrieval, Semantic description, Soft biometrics, Video surveillance BibRef

Zhang, S., Xie, Y., Wan, J., Xia, H., Li, S.Z., Guo, G.,
WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild,
MultMed(22), No. 2, February 2020, pp. 380-393.
IEEE DOI 2001
Benchmark testing, Detectors, Training, Urban areas, Cameras, Task analysis, Deep learning, Pedestrian detection, dataset, high density BibRef

Yang, Y.[Yang], Tan, Z.C.[Zi-Chang], Tiwari, P.[Prayag], Pandey, H.M.[Hari Mohan], Wan, J.[Jun], Lei, Z.[Zhen], Guo, G.D.[Guo-Dong], Li, S.Z.[Stan Z.],
Cascaded Split-and-Aggregate Learning with Feature Recombination for Pedestrian Attribute Recognition,
IJCV(129), No. 10, October 2021, pp. 2731-2744.
Springer DOI 2110
BibRef

Chandrasekar, K.S.[Karnam Silpaja], Geetha, P.[Planisamy],
Highly efficient neoteric histogram-entropy-based rapid and automatic thresholding method for moving vehicles and pedestrians detection,
IET-IPR(14), No. 2, February 2020, pp. 354-365.
DOI Link 2001
BibRef

Huang, E.[Enbo], Su, Z.[Zhuo], Zhou, F.[Fan], Wang, R.[Ruomei],
Learning rebalanced human parsing model from imbalanced datasets,
IVC(99), 2020, pp. 103928.
Elsevier DOI 2006
Human parsing, Semantic segmentation, Imbalanced datasets BibRef

Zhao, J.[Jian], Li, J.S.[Jian-Shu], Liu, H.Z.[Heng-Zhu], Yan, S.C.[Shui-Cheng], Feng, J.S.[Jia-Shi],
Fine-Grained Multi-human Parsing,
IJCV(128), No. 8-9, September 2020, pp. 2185-2203.
Springer DOI 2008
BibRef

Liu, S.[Si], Ren, G.H.[Guang-Hui], Sun, Y.[Yao], Wang, J.Q.[Jin-Qiao], Wang, C.H.[Chang-Hu], Li, B.[Bo], Yan, S.C.[Shui-Cheng],
Fine-Grained Human-Centric Tracklet Segmentation with Single Frame Supervision,
PAMI(44), No. 2, February 2022, pp. 610-621.
IEEE DOI 2201
Labeling, Object segmentation, Image segmentation, Task analysis, Semantics, Training, Face, Video object segmentation, human-centric, optical flow estimation BibRef

Chiang, S.H.[Sheng-Ho], Wang, T.[Tsaipei], Chen, Y.F.[Yi-Fu],
Efficient pedestrian detection in top-view fisheye images using compositions of perspective view patches,
IVC(105), 2021, pp. 104069.
Elsevier DOI 2101
Pedestrian detection, Fisheye cameras, Omnidirectional cameras BibRef

Sena, J.[Jessica], Jordão, A.[Artur], Schwartz, W.R.[William Robson],
A content-based late fusion approach applied to pedestrian detection,
JVCIR(77), 2021, pp. 103091.
Elsevier DOI 2106
Edestrian detection, Content-based fusion, Spatial consensus, Multiple detectors, Late fusion BibRef

Tian, W.[Wei], Deng, Z.[Zhenwen], Yin, D.[Dong], Zheng, Z.[Zehan], Huang, Y.[Yuyao], Bi, X.[Xin],
3D Pedestrian Detection in Farmland by Monocular RGB Image and Far-Infrared Sensing,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Zhang, X.Z.[Xiang-Zhou], Ma, B.P.[Bing-Peng], Chang, H.[Hong], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Location Sensitive Network for Human Instance Segmentation,
IP(30), 2021, pp. 7649-7662.
IEEE DOI 2109
Image segmentation, Prototypes, Heating systems, Task analysis, Semantics, Feature extraction, Detectors, points representation BibRef

Fan, H.N.[Hao-Nan], Hu, H.M.[Hai-Miao], Liu, S.L.[Shuai-Ling], Lu, W.Q.[Wei-Qing], Pu, S.L.[Shi-Liang],
Correlation Graph Convolutional Network for Pedestrian Attribute Recognition,
MultMed(24), 2022, pp. 49-60.
IEEE DOI 2202
Correlation, Task analysis, Lighting, Image resolution, Hair, Surveillance, Manifolds, Pedestrian Attribute Recognition, Hierarchical Relation BibRef

Tan, F.[Fang], Xia, Z.Q.[Zhao-Qiang], Ma, Y.P.[Yu-Peng], Feng, X.Y.[Xiao-Yi],
3D Sensor Based Pedestrian Detection by Integrating Improved HHA Encoding and Two-Branch Feature Fusion,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Shen, G.[Gelin], Yu, Y.[Yang], Tang, Z.R.[Zhi-Ri], Chen, H.Q.[Hao-Qiang], Zhou, Z.T.[Zong-Tan],
HQA-Trans: An end-to-end high-quality-awareness image translation framework for unsupervised cross-domain pedestrian detection,
IET-CV(16), No. 3, 2022, pp. 218-229.
DOI Link 2204
BibRef

Kumar, K.[Kaushal], Mishra, R.K.[Ritesh Kumar],
A Diagonally Oriented Novel Feature Extractor for Pedestrian Detection and Its Efficient Hardware Implementation,
CirSysVideo(32), No. 4, April 2022, pp. 2035-2042.
IEEE DOI 2204
Feature extraction, Hardware, Computer architecture, Histograms, Field programmable gate arrays, Mathematical model, pedestrian detection BibRef

Zhang, T.L.[Tian-Liang], Ye, Q.X.[Qi-Xiang], Zhang, B.C.[Bao-Chang], Liu, J.Z.[Jian-Zhuang], Zhang, X.P.[Xiao-Peng], Tian, Q.[Qi],
Feature Calibration Network for Occluded Pedestrian Detection,
ITS(23), No. 5, May 2022, pp. 4151-4163.
IEEE DOI 2205
Feature extraction, Calibration, Detectors, Deep learning, Visualization, Training, Object detection, Pedestrian detection, self-paced learning BibRef

Pahalawatta, K.[Kapila], Fourie, J.[Jaco], Potgieter, J.[Johan], Ascot-Evans, H.[Heath], Werner, A.[Armin],
Robust human instance segmentation in a challenging forest environment,
IVCNZ21(1-6)
IEEE DOI 2201
Image segmentation, Navigation, Image color analysis, Vegetation mapping, Forestry, Thermal sensors, Robot sensing systems BibRef

Hasan, I.[Irtiza], Liao, S.C.[Sheng-Cai], Li, J.[Jinpeng], Akram, S.U.[Saad Ullah], Shao, L.[Ling],
Generalizable Pedestrian Detection: The Elephant In The Room,
CVPR21(11323-11332)
IEEE DOI 2111
Training, Pipelines, Detectors, Benchmark testing, Video surveillance, Market research, Distance measurement BibRef

Wanchaitanawong, N.[Napat], Tanaka, M.[Masayuki], Shibata, T.[Takashi], Okutomi, M.[Masatoshi],
Multi-Modal Pedestrian Detection with Large Misalignment Based on Modal-Wise Regression and Multi-Modal IoU,
MVA21(1-6)
DOI Link 2109
Electric breakdown, Lighting, Robustness BibRef

Ge, Z.[Zheng], Hu, C.Y.[Chu-Yu], Huang, X.[Xin], Qiu, B.Q.[Bai-Qiao], Yoshie, O.[Osamu],
DualBox: Generating BBox Pair with Strong Correspondence via Occlusion Pattern Clustering and Proposal Refinement,
ICPR21(2097-2102)
IEEE DOI 2105
Pedestrians. Clustering algorithms, Pattern clustering, Detectors, Benchmark testing, Prediction algorithms, Non-Maximum-Suppression BibRef

Fang, P.F.[Peng-Fei], Ji, P.[Pan], Zhou, J.[Jieming], Petersson, L.[Lars], Harandi, M.[Mehrtash],
Channel Recurrent Attention Networks for Video Pedestrian Retrieval,
ACCV20(VI:427-443).
Springer DOI 2103
BibRef

Yang, X.Y.[Xing-Yi], Wang, Y.[Yong], Laganière, R.[Robert],
A Scale-aware Yolo Model for Pedestrian Detection,
ISVC20(II:15-26).
Springer DOI 2103
BibRef

Ghasemi, M., Varshosaz, M., Pirasteh, S.,
Evaluating Sector Ring Histogram of Oriented Gradients Filter In Locating Humans Within UAV Images,
ISPRS20(B2:23-27).
DOI Link 2012
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Zhou, K.L.[Kai-Lai], Chen, L.S.[Lin-Sen], Cao, X.[Xun],
Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems,
ECCV20(XVIII:787-803).
Springer DOI 2012
BibRef

Yang, L.[Lu], Song, Q.[Qing], Wang, Z.H.[Zhi-Hui], Hu, M.J.[Meng-Jie], Liu, C.[Chun], Xin, X.S.[Xue-Shi], Jia, W.H.[Wen-He], Xu, S.C.[Song-Cen],
Renovating Parsing R-CNN for Accurate Multiple Human Parsing,
ECCV20(XII: 421-437).
Springer DOI 2010
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Simon, J.[Jules], Bilodeau, G.A.[Guillaume-Alexandre], Steele, D.[David], Mahadik, H.[Harshad],
Color Inference from Semantic Labeling for Person Search in Videos,
ICIAR20(I:139-151).
Springer DOI 2007
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Aggarwal, S.[Surbhi], Babu, R.V.[R. Venkatesh], Chakraborty, A.[Anirban],
Text-based Person Search via Attribute-aided Matching,
WACV20(2606-2614)
IEEE DOI 2006
pedestrian images that best match a given text query. Feature extraction, Semantics, Task analysis, Training, Footwear, Visualization BibRef

Tang, C., Sheng, L., Zhang, Z., Hu, X.,
Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization,
ICCV19(4996-5005)
IEEE DOI 2004
image representation, learning (artificial intelligence), pedestrians, video surveillance BibRef

Vobecký, A., Uricár, M., Hurych, D., Škoviera, R.,
Advanced Pedestrian Dataset Augmentation for Autonomous Driving,
ADW19(2367-2372)
IEEE DOI 2004
pedestrians, pose estimation, occlusions, autonomous driving applications, people image generation, autonomous driving BibRef

Niu, K., Huang, Y., Wang, L.,
Fusing Two Directions in Cross-Domain Adaption for Real Life Person Search by Language,
WIDER19(1815-1818)
IEEE DOI 2004
image fusion, learning (artificial intelligence), natural language processing, neural nets, video surveillance, Image sentence matching BibRef

Liu, W.[Wei], Liao, S.C.[Sheng-Cai], Ren, W.Q.[Wei-Qiang], Hu, W.D.[Wei-Dong], Yu, Y.[Yinan],
High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection,
CVPR19(5182-5191).
IEEE DOI 2002
BibRef

Baeck, P.J., Lewyckyj, N., Beusen, B., Horsten, W., Pauly, K.,
Drone Based Near Real-time Human Detection With Geographic Localization,
Gi4DM19(49-53).
DOI Link 1912
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Li, T., Wan, W., Huang, Y., Chen, J., Hu, C., Ma, Y.,
Improving Human Parsing by Extracting Global Information Using the Non-Local Operation,
ICIP19(2961-2965)
IEEE DOI 1910
Deep Learning, Human Parsing, Semantic Segmentation, Non-local Operation BibRef

Fang, L.J.[Liang-Ji], Zhao, X.[Xu], Song, X.[Xiao], Zhang, S.Q.[Shi-Quan], Yang, M.[Ming],
Putting the Anchors Efficiently: Geometric Constrained Pedestrian Detection,
ACCV18(V:387-403).
Springer DOI 1906
BibRef

Peng, X., Murphey, Y., Stent, S., Li, Y., Zhao, Z.,
Spatial Focal Loss for Pedestrian Detection in Fisheye Imagery,
WACV19(561-569)
IEEE DOI 1904
cameras, learning (artificial intelligence), object detection, pedestrians, traffic engineering computing, camera system, Task analysis BibRef

Tamura, M., Horiguchi, S., Murakami, T.,
Omnidirectional Pedestrian Detection by Rotation Invariant Training,
WACV19(1989-1998)
IEEE DOI 1904
convolutional neural nets, image processing, object detection, pedestrians, omnidirectional pedestrian detection, Transforms BibRef

Wen, F.[Fang], Lin, Z.H.[Ze-Hang], Yang, Z.G.[Zhen-Guo], Liu, W.Y.[Wen-Yin],
Single-Stage Detector with Semantic Attention for Occluded Pedestrian Detection,
MMMod19(II:414-425).
Springer DOI 1901
BibRef

Zhang, T.R.[Tai-Ran], Lang, C.Y.[Cong-Yan], Xing, J.L.[Jun-Liang],
Realtime Human Segmentation in Video,
MMMod19(II:206-217).
Springer DOI 1901
BibRef

Chavdarova, T., Baqué, P., Bouquet, S., Maksai, A., Jose, C., Bagautdinov, T., Lettry, L., Fua, P., Van Gool, L.J., Fleuret, F.,
WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection,
CVPR18(5030-5039)
IEEE DOI 1812
Cameras, Benchmark testing, Computational modeling, Tracking, Synchronization, Detectors BibRef

Chang, Y., Chen, H., Chuang, J., Liao, I.,
Pedestrian Detection in Aerial Images Using Vanishing Point Transformation and Deep Learning,
ICIP18(1917-1921)
IEEE DOI 1809
Machine learning, Feature extraction, Transforms, Object detection, Drones, Image recognition, Computational modeling, vanishing point transformation BibRef

Ulutan, O., Riggan, B.S., Nasrabadi, N.M., Manjunath, B.S.,
An Order Preserving Bilinear Model for Person Detection in Multi-Modal Data,
WACV18(1160-1169)
IEEE DOI 1806
cameras, image fusion, image resolution, neural nets, object detection, sensor fusion, spatiotemporal phenomena, vectors, Visualization BibRef

Krams, O., Kiryati, N.,
People detection in top-view fisheye imaging,
AVSS17(1-6)
IEEE DOI 1806
Jacobian matrices, calibration, cameras, feature extraction, image reconstruction, image sensors, lenses, object detection, Standards BibRef

Kniaz, V.V., Fedorenko, V.V.,
An Algorithm for Pedestrian Detection in Multispectral Image Sequences,
PTVSBB17(73-77).
DOI Link 1805
BibRef

Zhang, H., Hu, X., Zhuo, L., Zhang, J.,
Pedestrian Detection Based on Imbalance Prior for Surveillance Video,
DICTA17(1-7)
IEEE DOI 1804
edge detection, feature extraction, image classification, image colour analysis, image resolution, pedestrians, Surveillance BibRef

Williams, J., Carneiro, G., Suter, D.,
Region of Interest Autoencoders with an Application to Pedestrian Detection,
DICTA17(1-8)
IEEE DOI 1804
image reconstruction, learning (artificial intelligence), object detection, Training BibRef

García-Martín, A., San Miguel, J.C.,
Adaptive people detection based on cross-correlation maximization,
ICIP17(3385-3389)
IEEE DOI 1803
Correlation, Detectors, Mutual information, Power capacitors, Runtime, Standards, Training, Detector adaptation, Thresholds BibRef

Yan, Y., Xu, M., Smith, J.S.,
Multiview pedestrian localisation via a prime candidate chart based on occupancy likelihoods,
ICIP17(2334-2338)
IEEE DOI 1803
Benchmark testing, Cameras, Color, Phantoms, Robustness, Transmission line matrix methods, image fusion, visual surveillance BibRef

Ji, Z., Zheng, W., Pang, Y.,
Deep pedestrian attribute recognition based on LSTM,
ICIP17(151-155)
IEEE DOI 1803
Attribute prediction, LSTM, Pedestrian attribute recognition, Visual surveillance. BibRef

Chien, J.T., Chou, C.J., Chen, D.J., Chen, H.T.,
Detecting Nonexistent Pedestrians,
CVRoads17(182-189)
IEEE DOI 1802
Head, Image segmentation, Training, Training data BibRef

Zhou, H.Y., Gao, B.B., Wu, J.,
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors,
ICCV17(3525-3533)
IEEE DOI 1802
image classification, object detection, AF classifier, Adaptive Feeding, Caltech Pedestrian dataset, MS COCO dataset, Real-time systems BibRef

Gabriel, E.[Eric], Schramm, H.[Hauke], Meyer, C.[Carsten],
Analysis of the Discriminative Generalized Hough Transform for Pedestrian Detection,
CIAP17(II:104-115).
Springer DOI 1711
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

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

Cao, C., Wang, Y.[Yu], Kato, J.[Jien], Zhang, G.W.[Guan-Wen], Mase, K.J.[Ken-Ji],
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.W.[Guan-Wen], Mase, K.J.[Ken-Ji],
Add-On Strategies for Fine-Grained Pedestrian Classification,
DICTA16(1-6)
IEEE DOI 1701
Feature extraction 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
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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
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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

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

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

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
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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.Y.[Guo-Yue], 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

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
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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

Sager, H.[Hisham], Hoff, W.A.[William A.],
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

Barbosa-Anda, F.R., Lerasle, F.[Frédéric], Briand, C., Mekonnen, A.A.[Alhayat Ali],
Soft-Cascade Learning with Explicit Computation Time Considerations,
WACV18(1234-1243)
IEEE DOI 1806
computational complexity, image classification, learning (artificial intelligence), object detection, Tuning BibRef

Rujikietgumjorn, S.[Sitapa], Collins, R.T.[Robert T.],
Optimized Pedestrian Detection for Multiple and Occluded People,
CVPR13(3690-3697)
IEEE DOI 1309
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Hosang, J.[Jan], Benenson, R.[Rodrigo], Schiele, B.[Bernt],
How good are detection proposals, really?,
BMVC14(xx-yy).
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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
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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
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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
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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
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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
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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
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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
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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
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Huang, P.J.[Po-Jui], Chen, D.Y.[Duan-Yu],
Robust wheelchair pedestrian detection using sparse representation,
VCIP12(1-5).
IEEE DOI 1302
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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],
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BMVC12(10).
DOI Link 1301
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Evans, M.[Murray], Li, L.Z.[Long-Zhen], Ferryman, J.M.[James M.],
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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
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Ding, Y.Y.[Yuan-Yuan], Xiao, J.[Jing],
Contextual boost for pedestrian detection,
CVPR12(2895-2902).
IEEE DOI 1208
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Munaro, M.[Matteo], Cenedese, A.[Angelo],
Scene specific people detection by simple human interaction,
HICV11(1250-1255).
IEEE DOI 1201
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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
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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
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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
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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
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El Guebaly, T.[Tarek], Bouguila, N.[Nizar],
A nonparametric Bayesian approach for enhanced pedestrian detection and foreground segmentation,
OTCBVS11(21-26).
IEEE DOI 1106
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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
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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
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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
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Middleton, L.[Lee], Snowdon, J.R.[James R.],
Histogram of confidences for person detection,
ICIP10(1841-1844).
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.L.[Jia-Li], 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
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Heimonen, T.A.[Teuvo Antero], Heikkila, J.[Janne],
A Human Detection Framework for Heavy Machinery,
ICPR10(416-419).
IEEE DOI 1008
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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
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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
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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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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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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
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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
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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
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Yu, L.P.[Li-Ping], Yao, W.T.[Wen-Tao],
Pedestrian Detection Fusion Method Based on Mean Shift,
ICMV09(204-207).
IEEE DOI 0912
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Rapus, M.[Martin], Munder, S.[Stefan], Baratoff, G.[Gregory], Denzler, J.[Joachim],
Pedestrian Detection by Probabilistic Component Assembly,
DAGM09(91-100).
Springer DOI 0909
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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
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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
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Thome, N.[Nicolas], Ambellouis, S.[Sebastien],
A bottom-up, view-point invariant human detector,
ICPR08(1-4).
IEEE DOI 0812
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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
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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
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Abramson, Y., Steux, B.,
Hardware-friendly pedestrian detection and impact prediction,
IVS04(590-595).
IEEE DOI 0411
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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
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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
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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

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
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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
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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
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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],
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
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Sivic, J., Zitnick, C.L., Szeliski, R.S.[Richard S.],
Finding people in repeated shots of the same scene,
BMVC06(III:909).
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Davis, L.S.[Larry S.],
Segmenting people in small groups,
VSSN06(1-2).
WWW Link. 0701
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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
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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).
IEEE DOI 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).
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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
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Lefee, D., Mousset, S., Bertozzi, M., Bensrhair, A.,
Cooperation of passive vision systems in detection and tracking of pedestrians,
IVS04(768-773).
IEEE DOI 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

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

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

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 .