Appearance Tracking for Video Surveillance

We present an algorithm which tracks multiple objects for video surveillance applications. This algorithm is based on a Bayesian framework and a Particle filter. In order to use this method in practical applications we define a statistical model of the object appearance to build a robust likelihood function. The tracking process is only based on image data, therefore, a previous step to learn the object shape and their motion parameters is not necessary. Using the localization results, we can define a prior density which is used to initialize the algorithm. Finally, our method has been proved successfully in several sequences and its performance is more accurate than classical filters.

[1]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

[2]  Michael Isard,et al.  A mixed-state condensation tracker with automatic model-switching , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

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

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

[5]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  David J. Fleet,et al.  Probabilistic detection and tracking of motion discontinuities , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  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).

[8]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[9]  Y. Ho,et al.  A Bayesian approach to problems in stochastic estimation and control , 1964 .

[10]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.