Real-time Multiple Head Shape Detection and Tracking System with Decentralized Trackers

This paper presents a robust human tracking system which incorporates automatic detection of head shape objects with decentralized tracking approach. A fast and robust probabilistic shape contour matching algorithm is applied to the input image frame to detect and locate head shape objects. The detected objects are then tracked by decentralized trackers. Here, a decentralized tracker refers to the tracker that tracks exactly one object. Essentially, each newly detected object will instantiate an individual tracker, which tracks the object and destroys itself when the object disappears. Two trackers communicate with each other only when they are getting close enough. This approach simplifies the competition of targets between trackers, and is more efficient than the centralized approach whose time complexity is greatly depends on the number of tracked objects. The system has been tested with several challenging digital surveillance video sequences, and the results show the robustness and the efficiency of the system under crowded and clutter environment

[1]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.

[2]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[3]  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.

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

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

[6]  Michael Isard,et al.  Active Contours , 2000, Springer London.

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

[8]  Patrick Pérez,et al.  Maintaining multimodality through mixture tracking , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[9]  Andrew Blake,et al.  A Probabilistic Exclusion Principle for Tracking Multiple Objects , 2004, International Journal of Computer Vision.

[10]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[11]  Cristian Sminchisescu,et al.  Discriminative density propagation for 3D human motion estimation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[13]  Ramakant Nevatia,et al.  Tracking multiple humans in crowded environment , 2004, CVPR 2004.

[14]  Andrew Blake,et al.  A Probabilistic Exclusion Principle for Tracking Multiple Objects , 2000, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  Ying Wu,et al.  Decentralized multiple target tracking using netted collaborative autonomous trackers , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  John MacCormick.  Stochastic algorithms for visual tracking: probabilistic modelling and stochastic algorithms for visual localisation and tracking , 2000 .

[17]  Kky Wong,et al.  Real time human body tracking using wavenet , 2004 .

[18]  Francis Y. L. Chin,et al.  Explicit contour model for vehicle tracking with automatic hypothesis validation , 2005, IEEE International Conference on Image Processing 2005.