Social grouping for target handover in multi-view video

This paper uses a social grouping model to improve target handover across multiple non-overlapping cameras to enable wide-area video understanding. Prior work focuses on modeling appearance and spatial-temporal cues for target handover. In cameras with different conditions, these cues are weak, at best. We provide a complete generative social grouping model which generalizes a recent single-camera case. Our extension requires strengthening the probabilistic interpretations and the resulting optimization over track handovers and social groupings can be formulated in terms of standard fast algorithms. We demonstrate the effectiveness of the method over existing techniques on challenging real-world multi-camera video.

[1]  Munchurl Kim,et al.  Graph-based object detection and tracking in H.264/AVC bitstreams for surveillance video , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[2]  Yi-Ping Hung,et al.  An adaptive learning method for target tracking across multiple cameras , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  D. Helbing,et al.  The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics , 2010, PloS one.

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

[5]  Andrew Gilbert,et al.  Tracking Objects Across Cameras by Incrementally Learning Inter-camera Colour Calibration and Patterns of Activity , 2006, ECCV.

[6]  Zhen Qin,et al.  Improving multi-target tracking via social grouping , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[8]  Luc Van Gool,et al.  Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings , 2010, ECCV.

[9]  Ramin Zabih,et al.  Bayesian multi-camera surveillance , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[10]  Mubarak Shah,et al.  Tracking across multiple cameras with disjoint views , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Michael J. Brooks,et al.  A Stochastic Approach to Tracking Objects Across Multiple Cameras , 2004, Australian Conference on Artificial Intelligence.

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

[13]  Mubarak Shah,et al.  Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views , 2008, Comput. Vis. Image Underst..

[14]  Tiziana D'Orazio,et al.  Color Brightness Transfer Function evaluation for non overlapping multi camera tracking , 2009, 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC).

[15]  Andrea Cavallaro,et al.  Multi-camera tracking using a Multi-Goal Social Force Model , 2013, Neurocomputing.

[16]  Ramakant Nevatia,et al.  Inter-camera Association of Multi-target Tracks by On-Line Learned Appearance Affinity Models , 2010, ECCV.

[17]  Margrit Betke,et al.  Evaluation of tracking methods for human-computer interaction , 2002, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings..

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

[19]  Luc Van Gool,et al.  You'll never walk alone: Modeling social behavior for multi-target tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Marshall F. Tappen,et al.  Learning pedestrian dynamics from the real world , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[21]  Bir Bhanu,et al.  VideoWeb Dataset for Multi-camera Activities and Non-verbal Communication , 2011 .