Person Re-Identification

For making sense of the vast quantity of visual data generated by the rapid expansion of large-scale distributed multi-camera systems, automated person re-identification is essential. However, it poses a significant challenge to computer vision systems. Fundamentally, person re-identification requires to solve two difficult problems of ‘finding needles in haystacks’ and ‘connecting the dots’ by identifying instances and associating the whereabouts of targeted people travelling across large distributed space–time locations in often crowded environments. This capability would enable the discovery of, and reasoning about, individual-specific long-term structured activities and behaviours. Whilst solving the person re-identification problem is inherently challenging, it also promises enormous potential for a wide range of practical applications, ranging from security and surveillance to retail and health care. As a result, the field has drawn growing and wide interest from academic researchers and industrial developers. This chapter introduces the re-identification problem, highlights the difficulties in building person re-identification systems, and presents an overview of recent progress and the state-of-the-art approaches to solving some of the fundamental challenges in person re-identification, benefiting from research in computer vision, pattern recognition and machine learning, and drawing insights from video analytics system design considerations for engineering practical solutions. It also provides an introduction of the contributing chapters of this book. S. Gong (B) · T. M. Hospedales Queen Mary University of London, London, UK e-mail: sgg@eecs.qmul.ac.uk M. Cristani University of Verona and Istituto Italiano di Tecnologia, Verona, Italy e-mail: marco.cristani@univr.it C. C. Loy The Chinese University of Hong Kong, Shatin, Hong Kong e-mail: ccloy@ie.cuhk.edu.hk T. M. Hospedales e-mail: tmh@eecs.qmul.ac.uk S. Gong et al. (eds.), Person Re-Identification, 1 Advances in Computer Vision and Pattern Recognition, DOI: 10.1007/978-1-4471-6296-4_1, © Springer-Verlag London 2014

[1]  Slawomir Bak,et al.  Learning to Match Appearances by Correlations in a Covariance Metric Space , 2012, ECCV.

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

[3]  Fabio Roli,et al.  Fast person re-identification based on dissimilarity representations , 2012, Pattern Recognit. Lett..

[4]  Vittorio Ferrari,et al.  Better Appearance Models for Pictorial Structures , 2009, BMVC.

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

[6]  Cordelia Schmid,et al.  Coloring Local Feature Extraction , 2006, ECCV.

[7]  Peter H. Tu,et al.  Appearance-based person reidentification in camera networks: problem overview and current approaches , 2011, J. Ambient Intell. Humaniz. Comput..

[8]  Larry S. Davis,et al.  Learning Discriminative Appearance-Based Models Using Partial Least Squares , 2009, 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing.

[9]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2004, International Journal of Computer Vision.

[10]  Vittorio Murino,et al.  Person re-identification with a PTZ camera: An introductory study , 2013, 2013 IEEE International Conference on Image Processing.

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

[12]  Yuandong Tian,et al.  Exploring the Spatial Hierarchy of Mixture Models for Human Pose Estimation , 2012, ECCV.

[13]  W. Eric L. Grimson,et al.  Inference of non-overlapping camera network topology by measuring statistical dependence , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[14]  S. Ramya.  Web Image Re-Ranking Using Query-Specific Semantic Signatures , 2015 .

[15]  Omar Javed,et al.  Self Calibrating Visual Sensor Networks , 2008, 2008 IEEE Workshop on Applications of Computer Vision.

[16]  Per-Erik Forssén,et al.  Maximally Stable Colour Regions for Recognition and Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

[18]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[19]  Chris Stauffer,et al.  Learning to Track Objects Through Unobserved Regions , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[20]  W. Eric L. Grimson,et al.  Correspondence-free multi-camera activity analysis and scene modeling , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Meng Wang,et al.  Transferring a generic pedestrian detector towards specific scenes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[23]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[24]  Andrea Cavallaro,et al.  Person re-identification in crowd , 2012, Pattern Recognit. Lett..

[25]  Qiang Ji,et al.  Spatio-Temporal Context for Robust Multitarget Tracking , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Xiaogang Wang,et al.  Unsupervised Salience Learning for Person Re-identification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Richard I. Hartley,et al.  Person Reidentification Using Spatiotemporal Appearance , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[28]  Fatih Murat Porikli,et al.  Inter-camera color calibration by correlation model function , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[29]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[30]  Slawomir Bak,et al.  Multiple-shot human re-identification by Mean Riemannian Covariance Grid , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[31]  Slawomir Bak,et al.  Person Re-identification Using Spatial Covariance Regions of Human Body Parts , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[32]  Jian Sun,et al.  An associate-predict model for face recognition , 2011, CVPR 2011.

[33]  Shaogang Gong,et al.  Multi-camera Matching using Bi-Directional Cumulative Brightness Transfer Functions , 2008, BMVC.

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

[35]  Vittorio Murino,et al.  Semi-supervised multi-feature learning for person re-identification , 2013, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[36]  Yi Yang,et al.  Articulated pose estimation with flexible mixtures-of-parts , 2011, CVPR 2011.

[37]  Xiaogang Wang,et al.  Shape and Appearance Context Modeling , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[38]  Antonio Criminisi,et al.  Object categorization by learned universal visual dictionary , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[39]  Jitendra Malik,et al.  Discriminative Decorrelation for Clustering and Classification , 2012, ECCV.

[40]  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).

[41]  Shaogang Gong,et al.  Person Re-Identification by Support Vector Ranking , 2010, BMVC.

[42]  Alessandro Perina,et al.  Person re-identification by symmetry-driven accumulation of local features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[43]  Alessandro Perina,et al.  Multiple-shot person re-identification by chromatic and epitomic analyses , 2012, Pattern Recognit. Lett..

[44]  Vittorio Murino,et al.  A unifying framework for vector-valued manifold regularization and multi-view learning , 2013, ICML.

[45]  Hai Tao,et al.  Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .

[46]  Alessio Del Bue,et al.  Re-identification with RGB-D Sensors , 2012, ECCV Workshops.

[47]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[48]  Vittorio Murino,et al.  Symmetry-driven accumulation of local features for human characterization and re-identification , 2013, Comput. Vis. Image Underst..

[49]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[50]  Richard Szeliski,et al.  Finding People in Repeated Shots of the Same Scene , 2006, BMVC.

[51]  Slawomir Bak,et al.  Person Re-identification Using Haar-based and DCD-based Signature , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[52]  Larry S. Davis,et al.  Learning Pairwise Dissimilarity Profiles for Appearance Recognition in Visual Surveillance , 2008, ISVC.

[53]  Fabio Roli,et al.  A Multiple Component Matching Framework for Person Re-identification , 2011, ICIAP.

[54]  Massimo Piccardi,et al.  Tracking people across disjoint camera views by an illumination-tolerant appearance representation , 2007, Machine Vision and Applications.

[55]  Hai Tao,et al.  Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features , 2008, ECCV.

[56]  Visvanathan Ramesh,et al.  Sudden illumination change detection using order consistency , 2004, Image Vis. Comput..

[57]  S. Gong,et al.  Multi-camera Matching under Illumination Change Over Time , 2008 .

[58]  Vittorio Murino,et al.  Custom Pictorial Structures for Re-identification , 2011, BMVC.

[59]  Fabien Moutarde,et al.  Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

[60]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[61]  Deva Ramanan,et al.  Learning to parse images of articulated bodies , 2006, NIPS.

[62]  Senem Velipasalar,et al.  Real-time distributed tracking with non-overlapping cameras , 2010, 2010 IEEE International Conference on Image Processing.

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

[64]  David A. Forsyth,et al.  Tracking People by Learning Their Appearance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[65]  Dimitrios Makris,et al.  Bridging the gaps between cameras , 2004, CVPR 2004.

[66]  Shaogang Gong,et al.  Associating Groups of People , 2009, BMVC.