A Framework to Integrate Particle Filters for Robust Tracking in Non-stationary Environments

In this paper we propose a new framework to integrate several particle filters, in order to obtain a robust tracking system able to cope with abrupt changes of illumination and position of the target. The proposed method is analytically justified and allows to build a tracking procedure that adapts online and simultaneously the colorspace where the image points are represented, the color distributions of the object and background and the contour of the object.

[1]  Ehud Rivlin,et al.  A probabilistic framework for combining tracking algorithms , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

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

[3]  Jochen Triesch,et al.  Democratic Integration: Self-Organized Integration of Adaptive Cues , 2001, Neural Computation.

[4]  Luc Van Gool,et al.  An adaptive color-based particle filter , 2003, Image Vis. Comput..

[5]  Bernt Schiele,et al.  Towards robust multi-cue integration for visual tracking , 2001, Machine Vision and Applications.

[6]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

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

[8]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[9]  Francesc Moreno-Noguer,et al.  Fusion of a Multiple Hypotheses Color Model and Deformable Contours for Figure Ground Segmentation in Dynamic Environments , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[10]  Ramesh C. Jain,et al.  Difference and accumulative difference pictures in dynamic scene analysis , 1984, Image Vis. Comput..

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

[12]  Jan-Olof Eklundh,et al.  Probabilistic and Voting Approaches to Cue Integration for Figure-Ground Segmentation , 2002, ECCV.

[13]  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).