Real-time tracking of non-rigid objects using mean shift

A new method for real time tracking of non-rigid objects seen from a moving camera is proposed. The central computational module is based on the mean shift iterations and finds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution) and the target candidates is expressed by a metric derived from the Bhattacharyya coefficient. The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and efficient solution. The capability of the tracker to handle in real time partial occlusions, significant clutter, and target scale variations, is demonstrated for several image sequences.

[1]  T. Kailath.  The Divergence and Bhattacharyya Distance Measures in Signal Selection , 1967 .

[2]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[3]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[4]  F. D. Garber,et al.  The Quality of Training Sample Estimates of the Bhattacharyya Coefficient , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Kohji Fukunaga,et al.  Introduction to Statistical Pattern Recognition-Second Edition , 1990 .

[6]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[7]  Gérard G. Medioni,et al.  Finding Waldo, or focus of attention using local color information , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Alexandros Eleftheriadis,et al.  Automatic face location detection and tracking for model-assisted coding of video teleconferencing sequences at low bit-rates , 1995, Signal Process. Image Commun..

[9]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1995, Proceedings of IEEE International Conference on Computer Vision.

[10]  Rachid Deriche,et al.  Region tracking through image sequences , 1995, Proceedings of IEEE International Conference on Computer Vision.

[11]  H. Selbmann,et al.  Learning to recognize objects , 1999, Trends in Cognitive Sciences.

[12]  Ingemar J. Cox,et al.  An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  J. Simonoff.  Multivariate Density Estimation , 1996 .

[14]  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.

[15]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  James W. Davis,et al.  Real-time closed-world tracking , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Paul W. Fieguth,et al.  Color-based tracking of heads and other mobile objects at video frame rates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[19]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[20]  Yuntao Cui,et al.  Indoor monitoring via the collaboration between a peripheral sensor and a foveal sensor , 1998, Proceedings 1998 IEEE Workshop on Visual Surveillance.

[21]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[22]  James M. Rehg,et al.  A multiple hypothesis approach to figure tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[23]  Geodesic Active Regions for Motion Estimation and Tracking , 1999, ICCV.

[24]  Alan L. Yuille,et al.  Fundamental bounds on edge detection: an information theoretic evaluation of different edge cues , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[25]  Rómer Rosales,et al.  3D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[26]  Shaogang Gong,et al.  Tracking colour objects using adaptive mixture models , 1999, Image Vis. Comput..

[27]  Dorin Comaniciu,et al.  Mean shift analysis and applications , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.