Human detection from images and videos: A survey

The problem of human detection is to automatically locate people in an image or video sequence and has been actively researched in the past decade. This paper aims to provide a comprehensive survey on the recent development and challenges of human detection. Different from previous surveys, this survey is organised in the thread of human object descriptors. This approach has advantages in providing a thorough analysis of the state-of-the-art human detection methods and a guide to the selection of appropriate methods in practical applications. In addition, challenges such as occlusion and real-time human detection are analysed. The commonly used evaluation of human detection methods such as the datasets, tools, and performance measures are presented and future research directions are highlighted. HighlightsA review on the state-of-the-art of human detection.This review is organised in the thread of human object descriptors.Challenges such as occlusion and real-time human detection are analysed.The commonly used datasets, tools, and performance measures are presented.Open issues and future research directions are highlighted.A guide to the selection of detection methods for applications is provided.

[1]  Deva Ramanan,et al.  Exploring Weak Stabilization for Motion Feature Extraction , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Ran Xu,et al.  Human detection in images via L1-norm Minimization Learning , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  M. Pietikäinen,et al.  A discriminative feature space for detecting and recognizing faces , 2004, CVPR 2004.

[4]  Pietro Perona,et al.  Pedestrian detection: A benchmark , 2009, CVPR.

[5]  Duc Thanh Nguyen,et al.  An MRF-Poselets Model for Detecting Highly Articulated Humans , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[6]  Massimo Bertozzi,et al.  Shape-based pedestrian detection , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[7]  Larry S. Davis,et al.  View-based detection and analysis of periodic motion , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[8]  Alexei A. Efros,et al.  Geometric context from a single image , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[9]  Bernt Schiele,et al.  Disparity statistics for pedestrian detection: combining appearance, motion and stereo , 2010, ECCV 2010.

[10]  David Vázquez,et al.  Random Forests of Local Experts for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[11]  Cordelia Schmid,et al.  Human Detection Based on a Probabilistic Assembly of Robust Part Detectors , 2004, ECCV.

[12]  Bernt Schiele,et al.  Pictorial structures revisited: People detection and articulated pose estimation , 2009, CVPR.

[13]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[14]  Bill Triggs,et al.  Feature Sets and Dimensionality Reduction for Visual Object Detection , 2010, BMVC.

[15]  Dariu Gavrila,et al.  A mixed generative-discriminative framework for pedestrian classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  F. Attneave.  Some informational aspects of visual perception. , 1954, Psychological review.

[17]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[18]  Gunilla Borgefors,et al.  Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Sven J. Dickinson,et al.  TurboPixels: Fast Superpixels Using Geometric Flows , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Alexei A. Efros,et al.  Putting Objects in Perspective , 2006, CVPR.

[21]  Dumitru Erhan,et al.  Deep Neural Networks for Object Detection , 2013, NIPS.

[22]  Tommi S. Jaakkola,et al.  Tutorial on variational approximation methods , 2000 .

[23]  Dariu Gavrila,et al.  Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle , 2007, International Journal of Computer Vision.

[24]  Wanqing Li,et al.  Object detection using Non-Redundant Local Binary Patterns , 2010, 2010 IEEE International Conference on Image Processing.

[25]  J. Shotton,et al.  Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning , 2011 .

[26]  Wanqing Li,et al.  Detecting humans under occlusion using variational mean field method , 2011, 2011 18th IEEE International Conference on Image Processing.

[27]  Jenn-Jier James Lien,et al.  AdaBoost Learning for Human Detection Based on Histograms of Oriented Gradients , 2007, ACCV.

[28]  Qiang Wu,et al.  Fast and Accurate Human Detection Using a Cascade of Boosted MS-LBP Features , 2012, IEEE Signal Processing Letters.

[29]  Larry S. Davis,et al.  Hierarchical Part-Template Matching for Human Detection and Segmentation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[30]  Yang Song,et al.  Towards detection of human motion , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[31]  Tieniu Tan,et al.  Recent developments in human motion analysis , 2003, Pattern Recognit..

[32]  Daniel P. Huttenlocher,et al.  Efficient matching of pictorial structures , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[33]  Bernt Schiele,et al.  Learning People Detectors for Tracking in Crowded Scenes , 2013, 2013 IEEE International Conference on Computer Vision.

[34]  Wen Gao,et al.  Object detection using spatial histogram features , 2006, Image Vis. Comput..

[35]  Longin Jan Latecki,et al.  Contour Grouping Based on Contour-Skeleton Duality , 2009, International Journal of Computer Vision.

[36]  Bernt Schiele,et al.  Cross-Articulation Learning for Robust Detection of Pedestrians , 2006, DAGM-Symposium.

[37]  Luc Van Gool,et al.  Pedestrian detection at 100 frames per second , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Tomaso A. Poggio,et al.  A Trainable System for Object Detection , 2000, International Journal of Computer Vision.

[39]  Du Tran,et al.  Human Activity Recognition with Metric Learning , 2008, ECCV.

[40]  Xudong Jiang,et al.  Asymmetric Principal Component and Discriminant Analyses for Pattern Classification , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  Armin B. Cremers,et al.  Informed Haar-Like Features Improve Pedestrian Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Robert C. Bolles,et al.  Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching , 1977, IJCAI.

[43]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[44]  Patrick Sayd,et al.  Real-Time Humans Detection in Urban Scenes , 2007, BMVC.

[45]  Vittorio Murino,et al.  Part-based human detection on Riemannian manifolds , 2010, 2010 IEEE International Conference on Image Processing.

[46]  Rama Chellappa,et al.  Fast directional chamfer matching , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[47]  Pietro Perona,et al.  Multiple Component Learning for Object Detection , 2008, ECCV.

[48]  Luc Van Gool,et al.  Seeking the Strongest Rigid Detector , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[49]  Subhransu Maji,et al.  Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[50]  Michael J. Black,et al.  Automatic Detection and Tracking of Human Motion with a View-Based Representation , 2002, ECCV.

[51]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[52]  Hua Yu,et al.  Multi-posture Human Detection in Video Frames by Motion Contour Matching , 2007, ACCV.

[53]  R. Chapuis,et al.  Shape-based pedestrian detection and localization , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[54]  Wanqing Li,et al.  Human detection using local shape and Non-Redundant binary patterns , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[55]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[56]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[57]  Xudong Jiang,et al.  Human Detection by Quadratic Classification on Subspace of Extended Histogram of Gradients , 2014, IEEE Transactions on Image Processing.

[58]  Tieniu Tan,et al.  Boosted local structured HOG-LBP for object localization , 2011, CVPR 2011.

[59]  Vijay Kumar.  A Discriminative Voting Scheme for Object Detection using Hough Forests , 2010 .

[60]  Jiaolong Xu,et al.  Incremental Domain Adaptation of Deformable Part-based Models , 2014, BMVC.

[61]  David A. Forsyth,et al.  Probabilistic Methods for Finding People , 2001, International Journal of Computer Vision.

[62]  Ramakant Nevatia,et al.  Cluster Boosted Tree Classifier for Multi-View, Multi-Pose Object Detection , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[63]  Ramakant Nevatia,et al.  Bayesian human segmentation in crowded situations , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[64]  Dan Roth,et al.  Learning a Sparse Representation for Object Detection , 2002, ECCV.

[65]  D'arcy W. Thompson.  On growth and form i , 1943 .

[66]  Bernt Schiele,et al.  New features and insights for pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[67]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[68]  Bernt Schiele,et al.  A Performance Evaluation of Single and Multi-feature People Detection , 2008, DAGM-Symposium.

[69]  Shuicheng Yan,et al.  Discriminative local binary patterns for human detection in personal album , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[70]  Dariu Gavrila,et al.  An Experimental Study on Pedestrian Classification , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[71]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[72]  Yair Weiss,et al.  Learning object detection from a small number of examples: the importance of good features , 2004, CVPR 2004.

[73]  Baochang Zhang,et al.  Fast pedestrian detection with multi-scale orientation features and two-stage classifiers , 2010, 2010 IEEE International Conference on Image Processing.

[74]  Jing Xiao,et al.  Detection Evolution with Multi-order Contextual Co-occurrence , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[75]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[76]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[77]  Jitendra Malik,et al.  Poselets: Body part detectors trained using 3D human pose annotations , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[78]  Bernt Schiele,et al.  Multi-Aspect Detection of Articulated Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[79]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[80]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[81]  Dariu Gavrila,et al.  Real-time object detection for "smart" vehicles , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[82]  Yi Yang,et al.  Articulated Human Detection with Flexible Mixtures of Parts , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[83]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[84]  Larry S. Davis,et al.  A Comprehensive Evaluation Framework and a Comparative Study for Human Detectors , 2009, IEEE Transactions on Intelligent Transportation Systems.

[85]  David Gerónimo Gómez,et al.  2D-3D-based on-board pedestrian detection system , 2010, Comput. Vis. Image Underst..

[86]  Dariu Gavrila,et al.  Multi-feature hierarchical template matching using distance transforms , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[87]  Meng Wang,et al.  Scene-Specific Pedestrian Detection for Static Video Surveillance , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[88]  Fatih Murat Porikli,et al.  Human Detection via Classification on Riemannian Manifolds , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[89]  Mei-Chen Yeh,et al.  Fast Human Detection Using a Cascade of Histograms of Oriented Gradients , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[90]  A. Fascioli,et al.  Pedestrian Protection Systems : Issues , Survey , and Challenges , 2007 .

[91]  Andrew Blake,et al.  Multiscale Categorical Object Recognition Using Contour Fragments , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[92]  Ramakant Nevatia,et al.  Segmentation and Tracking of Multiple Humans in Crowded Environments , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[93]  David Vázquez,et al.  Occlusion Handling via Random Subspace Classifiers for Human Detection , 2014, IEEE Transactions on Cybernetics.

[94]  Richard Bowden,et al.  Detection and Tracking of Humans by Probabilistic Body Part Assembly , 2005, BMVC.

[95]  Jitendra Malik,et al.  Parts, objects and scenes: computational models and psychophysics , 2003 .

[96]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[97]  Xiaogang Wang,et al.  Joint Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[98]  Pietro Perona,et al.  The Fastest Pedestrian Detector in the West , 2010, BMVC.

[99]  Xuelong Li,et al.  Transfer learning for pedestrian detection , 2013, Neurocomputing.

[100]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[101]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[102]  Luc Van Gool,et al.  Depth and Appearance for Mobile Scene Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[103]  Shihong Lao,et al.  Multiview Pedestrian Detection Based on Vector Boosting , 2007, ACCV.

[104]  Larry S. Davis,et al.  A Pose-Invariant Descriptor for Human Detection and Segmentation , 2008, ECCV.

[105]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[106]  Christoph H. Lampert,et al.  Beyond sliding windows: Object localization by efficient subwindow search , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[107]  Daniel P. Huttenlocher,et al.  Distance Transforms of Sampled Functions , 2012, Theory Comput..

[108]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[109]  Tomás Lozano-Pérez,et al.  A Framework for Multiple-Instance Learning , 1997, NIPS.

[110]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

[111]  Mark Everingham,et al.  Implicit color segmentation features for pedestrian and object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[112]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[113]  Ramakant Nevatia,et al.  Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors , 2007, International Journal of Computer Vision.

[114]  Xiaogang Wang,et al.  A discriminative deep model for pedestrian detection with occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[115]  Bernt Schiele,et al.  Ten Years of Pedestrian Detection, What Have We Learned? , 2014, ECCV Workshops.

[116]  Xudong Jiang,et al.  Human detection using Discriminative and Robust Local Binary Pattern , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[117]  Bernt Schiele,et al.  Multi-cue onboard pedestrian detection , 2009, CVPR.

[118]  Vittorio Ferrari,et al.  We Are Family: Joint Pose Estimation of Multiple Persons , 2010, ECCV.

[119]  Larry S. Davis,et al.  Human detection using partial least squares analysis , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[120]  Xuelong Li,et al.  Rapid pedestrian detection in unseen scenes , 2011, Neurocomputing.

[121]  Thomas Brox,et al.  Training Deformable Object Models for Human Detection Based on Alignment and Clustering , 2014, ECCV.

[122]  Chung-Lin Huang,et al.  Gait Analysis For Human Identification Through Manifold Learning and HMM , 2007, 2007 IEEE Workshop on Motion and Video Computing (WMVC'07).

[123]  G. Johansson.  Visual motion perception. , 1975, Scientific American.

[124]  E. Rückert.  Detecting Pedestrians by Learning Shapelet Features , 2007 .

[125]  Antonio M. López,et al.  Pedestrian candidates generation using monocular cues , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[126]  Ran Xu,et al.  Cascaded L1-norm Minimization Learning (CLML) classifier for human detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[127]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[128]  Juergen Gall,et al.  Class-specific Hough forests for object detection , 2009, CVPR.

[129]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[130]  Li Yi,et al.  Object Detection Using Shape Codebook , 2007, BMVC.

[131]  Ram Nevatia,et al.  Part based object detection, segmentation, and tracking by boosting simple shape feature based weak classifiers , 2008 .

[132]  Mubarak Shah,et al.  Tracking and Object Classification for Automated Surveillance , 2002, ECCV.

[133]  Deva Ramanan,et al.  Histograms of Sparse Codes for Object Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[134]  Ramakant Nevatia,et al.  Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[135]  Xiaogang Wang,et al.  Single-Pedestrian Detection Aided by Multi-pedestrian Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[136]  Yang Song,et al.  Monocuolar Perception of Biological Motion - Clutter and Partial Occlusion , 2000, ECCV.

[137]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[138]  Rafael Grompone von Gioi,et al.  LSD: A Fast Line Segment Detector with a False Detection Control , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[139]  Ramakant Nevatia,et al.  Pedestrian Detection in Infrared Images based on Local Shape Features , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[140]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[141]  Gunnar Farnebäck,et al.  Two-Frame Motion Estimation Based on Polynomial Expansion , 2003, SCIA.

[142]  Johan Wagemans,et al.  Contour-based object identification and segmentation: Stimuli, norms and data, and software tools , 2004, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[143]  Tarak Gandhi,et al.  Pedestrian collision avoidance systems: a survey of computer vision based recent studies , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[144]  Lixin Fan,et al.  Pedestrian registration in static images with unconstrained background , 2003, Pattern Recognit..

[145]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[146]  Cordelia Schmid,et al.  Shape recognition with edge-based features , 2003, BMVC.

[147]  Dariu Gavrila,et al.  Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[148]  Long-Wen Chang,et al.  Human action recognition using temporal-state shape contexts , 2008, 2008 19th International Conference on Pattern Recognition.

[149]  Trevor Darrell,et al.  Fast concurrent object localization and recognition , 2009, CVPR.

[150]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[151]  Charless C. Fowlkes,et al.  Multiresolution Models for Object Detection , 2010, ECCV.

[152]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[153]  Wanqing Li,et al.  A part-based template matching method for multi-view human detection , 2009, 2009 24th International Conference Image and Vision Computing New Zealand.

[154]  Dariu Gavrila,et al.  Multi-cue pedestrian classification with partial occlusion handling , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[155]  Ran Xu,et al.  Pedestrian detection in images via cascaded L1-norm minimization learning method , 2012, Pattern Recognit..

[156]  B. Schiele,et al.  Combined Object Categorization and Segmentation With an Implicit Shape Model , 2004 .

[157]  Chunhua Shen,et al.  Pedestrian Detection Using Center-Symmetric Local Binary Patterns , 2010, International Conference on Information Photonics.

[158]  Wanqing Li,et al.  Inter-occlusion reasoning for human detection based on variational mean field , 2013, Neurocomputing.

[159]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[160]  Qixiang Ye,et al.  Human Detection in Images via Piecewise Linear Support Vector Machines , 2013, IEEE Transactions on Image Processing.

[161]  Geoff A. W. West,et al.  Salience Distance Transforms , 1995, CVGIP Graph. Model. Image Process..

[162]  Jitendra Malik,et al.  Using k-Poselets for Detecting People and Localizing Their Keypoints , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[163]  David A. McAllester,et al.  Object Detection with Grammar Models , 2011, NIPS.

[164]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[165]  Jordi Gonzàlez,et al.  Recursive Coarse-to-Fine Localization for Fast Object Detection , 2010, ECCV.

[166]  Wen Gao,et al.  Pedestrian detection via logistic multiple instance boosting , 2008, 2008 15th IEEE International Conference on Image Processing.

[167]  Daniela Moctezuma,et al.  HoGG: Gabor and HoG-based human detection for surveillance in non-controlled environments , 2013, Neurocomputing.

[168]  Wanqing Li,et al.  Human detection based on weighted template matching , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[169]  Dariu Gavrila,et al.  Pedestrian Detection and Tracking Using a Mixture of View-Based Shape–Texture Models , 2008, IEEE Transactions on Intelligent Transportation Systems.

[170]  Shihong Lao,et al.  Adaptive Contour Features in oriented granular space for human detection and segmentation , 2009, CVPR.

[171]  Dariu Gavrila,et al.  Integrated pedestrian classification and orientation estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[172]  Ronen Basri,et al.  Actions as Space-Time Shapes , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[173]  Larry S. Davis,et al.  Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[174]  M GavrilaDariu,et al.  Monocular Pedestrian Detection , 2009 .

[175]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[176]  Clark F. Olson,et al.  Automatic target recognition by matching oriented edge pixels , 1997, IEEE Trans. Image Process..

[177]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[178]  Larry S. Davis,et al.  Bilattice-based Logical Reasoning for Human Detection , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[179]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[180]  Fatih Murat Porikli,et al.  Pedestrian Detection via Classification on Riemannian Manifolds , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[181]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[182]  Subhransu Maji,et al.  Detecting People Using Mutually Consistent Poselet Activations , 2010, ECCV.

[183]  A. Shashua,et al.  Pedestrian detection for driving assistance systems: single-frame classification and system level performance , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[184]  Wanqing Li,et al.  Local intensity distribution descriptor for object detection , 2011 .

[185]  David A. Forsyth,et al.  Configuration Estimates Improve Pedestrian Finding , 2007, NIPS.

[186]  Wanqing Li,et al.  Human detection with contour-based local motion binary patterns , 2011, 2011 18th IEEE International Conference on Image Processing.

[187]  Larry S. Davis,et al.  Pedestrian Detection via Periodic Motion Analysis , 2007, International Journal of Computer Vision.

[188]  Chin-Chen Chang,et al.  A new edge detection approach based on image context analysis , 2006, Image Vis. Comput..

[189]  Shengcai Liao,et al.  Robust Multi-resolution Pedestrian Detection in Traffic Scenes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[190]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[191]  Dan Levi,et al.  Part-Based Feature Synthesis for Human Detection , 2010, ECCV.

[192]  Xiaogang Wang,et al.  Multi-stage Contextual Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[193]  Bernt Schiele,et al.  Multiple Object Class Detection with a Generative Model , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[194]  Pushmeet Kohli,et al.  On Detection of Multiple Object Instances Using Hough Transforms , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[195]  Bernt Schiele,et al.  An Evaluation of Local Shape-Based Features for Pedestrian Detection , 2005, BMVC.

[196]  Larry S. Davis,et al.  Multiple instance fFeature for robust part-based object detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[197]  Wanqing Li,et al.  On the Combination of Local Texture and Global Structure for Food Classification , 2010, 2010 IEEE International Symposium on Multimedia.

[198]  Zhuowen Tu,et al.  Feature Mining for Image Classification , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[199]  Andrew Blake,et al.  Contour-based learning for object detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[200]  Xiaogang Wang,et al.  Switchable Deep Network for Pedestrian Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[201]  Alex Pentland,et al.  Probabilistic Visual Learning for Object Representation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[202]  David Gerónimo Gómez,et al.  Survey of Pedestrian Detection for Advanced Driver Assistance Systems , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[203]  Liang Zhao,et al.  Stereo- and neural network-based pedestrian detection , 1999, Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).

[204]  Meng Wang,et al.  Automatic adaptation of a generic pedestrian detector to a specific traffic scene , 2011, CVPR 2011.

[205]  Cordelia Schmid,et al.  Combining efficient object localization and image classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[206]  Anil K. Jain,et al.  Object Matching Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[207]  Qingming Huang,et al.  Multiple Instance Boost Using Graph Embedding Based Decision Stump for Pedestrian Detection , 2008, ECCV.

[208]  A. Yezzi,et al.  Local or Global Minima: Flexible Dual-Front Active Contours , 2007 .

[209]  Andrew Zisserman,et al.  Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection , 2008, International Journal of Computer Vision.

[210]  Mubarak Shah,et al.  Detecting and segmenting humans in crowded scenes , 2007, ACM Multimedia.

[211]  Wanqing Li,et al.  An Improved Template Matching Method for Object Detection , 2009, ACCV.

[212]  Xuelong Li,et al.  Detection of Sudden Pedestrian Crossings for Driving Assistance Systems , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[213]  Larry S. Davis,et al.  Multiple instance fFeature for robust part-based object detection , 2009, CVPR.

[214]  Navneet Dalal,et al.  Finding People in Images and Videos , 2006 .

[215]  Gang Song,et al.  Object Detection Combining Recognition and Segmentation , 2007, ACCV.

[216]  Arthur Daniel Costea,et al.  Word Channel Based Multiscale Pedestrian Detection without Image Resizing and Using Only One Classifier , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[217]  Yann LeCun,et al.  Pedestrian Detection with Unsupervised Multi-stage Feature Learning , 2012, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[218]  Wanqing Li,et al.  A novel template matching method for human detection , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[219]  Jing Xiao,et al.  Contextual boost for pedestrian detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[220]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[221]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[222]  Massimo Bertozzi,et al.  Stereo Vision-based approaches for Pedestrian Detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[223]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[224]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[225]  Chu-Song Chen,et al.  A Cascade of Feed-Forward Classifiers for Fast Pedestrian Detection , 2007, ACCV.

[226]  Ramakant Nevatia,et al.  Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[227]  J. Grahn,et al.  Using SVM for Efficient Detection of Human Motion , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[228]  Cordelia Schmid,et al.  Dense Trajectories and Motion Boundary Descriptors for Action Recognition , 2013, International Journal of Computer Vision.

[229]  Bernt Schiele,et al.  Pedestrian detection in crowded scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[230]  Tieniu Tan,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[231]  William Rucklidge,et al.  Locating objects using the Hausdorff distance , 1995, Proceedings of IEEE International Conference on Computer Vision.

[232]  Paul E. Rybski,et al.  Real-time pedestrian detection with deformable part models , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[233]  A. Broggi,et al.  A tool for vision based pedestrian detection performance evaluation , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[234]  Bernt Schiele,et al.  Detection and Tracking of Occluded People , 2014, International Journal of Computer Vision.

[235]  Jitendra Malik,et al.  Object detection using a max-margin Hough transform , 2009, CVPR.

[236]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[237]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[238]  Meng Wang,et al.  Deep Learning of Scene-Specific Classifier for Pedestrian Detection , 2014, ECCV.

[239]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[240]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[241]  Chunhua Shen,et al.  Pyramid Center-Symmetric Local Binary/Trinary Patterns for Effective Pedestrian Detection , 2010, ACCV.

[242]  Wanqing Li,et al.  A novel shape-based non-redundant local binary pattern descriptor for object detection , 2013, Pattern Recognit..

[243]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[244]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[245]  Dumitru Erhan,et al.  Scalable Object Detection Using Deep Neural Networks , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[246]  H. Bischof,et al.  Fast human detection in crowded scenes by contour integration and local shape estimation , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[247]  Dariu Gavrila,et al.  A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[248]  Wenyu Liu,et al.  Human Detection Using Learned Part Alphabet and Pose Dictionary , 2014, ECCV.

[249]  Xiaogang Wang,et al.  Modeling Mutual Visibility Relationship in Pedestrian Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.