Contour-based object tracking with occlusion handling in video acquired using mobile cameras

We propose a tracking method which tracks the complete object regions, adapts to changing visual features, and handles occlusions. Tracking is achieved by evolving the contour from frame to frame by minimizing some energy functional evaluated in the contour vicinity defined by a band. Our approach has two major components related to the visual features and the object shape. Visual features (color, texture) are modeled by semiparametric models and are fused using independent opinion polling. Shape priors consist of shape level sets and are used to recover the missing object regions during occlusion. We demonstrate the performance of our method in real sequences with and without object occlusions.

[1]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  J. Sethian Level set methods : evolving interfaces in geometry, fluid mechanics, computer vision, and materials science , 1996 .

[3]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, 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]  Alex M. Andrew,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (2nd edition) , 2000 .

[7]  Rachid Deriche,et al.  Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Andrew Blake,et al.  A Probabilistic Background Model for Tracking , 2000, ECCV.

[9]  Guillermo Sapiro,et al.  Morphing Active Contours , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  A. Yezzi,et al.  A variational framework for joint segmentation and registration , 2001, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001).

[11]  G. Aubert,et al.  Video object segmentation using Eulerian region-based active contours , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[12]  Daniel Cremers,et al.  Nonlinear Shape Statistics in Mumford-Shah Based Segmentation , 2002, ECCV.

[13]  Abdol-Reza Mansouri,et al.  Region Tracking via Level Set PDEs without Motion Computation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  David J. Fleet,et al.  Robust Online Appearance Models for Visual Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Daniel Cremers,et al.  Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional , 2002, International Journal of Computer Vision.

[17]  Rachid Deriche,et al.  Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation , 2002, International Journal of Computer Vision.

[18]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[19]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.