Multiple Person Tracking by Spatiotemporal Tracklet Association

In the field of video surveillance, multiple object tracking is a challenging problem in the real application. In this paper, we propose a multiple object tracking method by spatiotemporal tracklet association. Firstly, reliable tracklets, the fragments of the entire trajectory of individual object movement, are generated by frame-wise association between object localization results in the neighbor frames. To avoid the negative influence of occlusion on reliable tracklet generation, part-based similarity computation is performed. Secondly, the produced tracklets are associated considering both spatial and temporal constrains to output the entire trajectory for individual person. Especially, we formulate the task of spatiotemporal multiple tracklet matching into a Maximum A Posterior (MAP) problem in the form of Markov Chain with spatiotemporal context constraints. The experiment on PETS 2012 dataset demonstrates the superiority of the proposed method.

[1]  Fuji Ren,et al.  Detect and track the dynamic deformation human body with the active shape model modified by motion vectors , 2011, 2011 IEEE International Conference on Cloud Computing and Intelligence Systems.

[2]  Lijun Guo,et al.  A new method combining HOG and Kalman filter for video-based human detection and tracking , 2010, 2010 3rd International Congress on Image and Signal Processing.

[3]  F. Fleuret,et al.  Multiple object tracking using flow linear programming , 2009, 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance.

[4]  Yaser Sheikh,et al.  Object tracking across multiple independently moving airborne cameras , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

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

[6]  Laurent D. Cohen,et al.  Single quantum dot tracking based on perceptual Grouping using minimal paths in a spatiotemporal volume , 2005, IEEE Transactions on Image Processing.

[7]  Lale Akarun,et al.  Real-time pose tracking based on a 3D skeletal model using multiple cameras , 2011, 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU).

[8]  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).

[9]  Takeo Kanade,et al.  Reliably Tracking Partially Overlapping Neural Stem Cells in DIC Microscopy Image Sequences , 2009 .

[10]  Ramakant Nevatia,et al.  Robust Object Tracking by Hierarchical Association of Detection Responses , 2008, ECCV.

[11]  James Black,et al.  Multi view image surveillance and tracking , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[12]  Sethuraman Panchanathan,et al.  Framework for performance evaluation of face recognition algorithms , 2002, SPIE ITCom.

[13]  Kai Oliver Arras,et al.  People detection in RGB-D data , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  H. Kuhn.  The Hungarian method for the assignment problem , 1955 .

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

[16]  Atsushi Yamashita,et al.  Human tracking with multiple cameras based on face detection and mean shift , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[17]  Ramakant Nevatia,et al.  Global data association for multi-object tracking using network flows , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Jing Zhang,et al.  Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Afshin Dehghan,et al.  Part-based multiple-person tracking with partial occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Lu Wang,et al.  Three-Dimensional Model-Based Human Detection in Crowded Scenes , 2012, IEEE Transactions on Intelligent Transportation Systems.

[21]  Dar-Shyang Lee,et al.  Improved Adaptive Mixture Learning for Robust Video Background Modeling , 2002, MVA.

[22]  Justus H. Piater,et al.  Multi-camera People Tracking by Collaborative Particle Filters and Principal Axis-Based Integration , 2007, ACCV.

[23]  Jake K. Aggarwal,et al.  Full-motion recovery from multiple video cameras applied to face tracking and recognition , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[24]  James J. Little,et al.  A Linear Programming Approach for Multiple Object Tracking , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Larry S. Davis,et al.  Probabilistic framework for segmenting people under occlusion , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.