A Cascading Framework of Contour Motion and Deformation Estimation for Non-Rigid Object Tracking

This paper mainly focuses on applications for non-rigid contour tracking in heavily cluttered background scenes. Based on the properties of non-rigid contour movements, a cascading framework for estimating contour motion and deformation is proposed. We solve the non-rigid contour tracking problem by decomposing it into three sub problems: motion estimation, deformation estimation, and shape regulation. First, we employ a particle filter to estimate the global motion parameters of the affine transform between successive frames. Then we generate a deformation probabilistic map to deform the contour. To improve the robustness, multiple cues are used for deformation probability estimation. Finally, we use a shape prior model to constrain the deformed contour. This enables us to retrieve the occluded parts of the contours and accurately track them while allowing shape changes specific to the given object types. Our experiments show that the proposed algorithm significantly improves the tracker performance.

[1]  Kristine L. Bell,et al.  A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking , 2007 .

[2]  Oncel Tuzel,et al.  Bayesian background modeling for foreground detection , 2005, VSSN@MM.

[3]  Namrata Vaswani,et al.  Particle filtering for geometric active contours with application to tracking moving and deforming objects , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Dorin Comaniciu,et al.  An information fusion framework for robust shape tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Patrick Pérez,et al.  Data fusion for visual tracking with particles , 2004, Proceedings of the IEEE.

[6]  Rama Chellappa,et al.  Visual tracking and recognition using appearance-adaptive models in particle filters , 2004, IEEE Transactions on Image Processing.

[7]  Xin Li,et al.  Contour-based object tracking with occlusion handling in video acquired using mobile cameras , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Stefano Soatto,et al.  Deformotion: Deforming Motion, Shape Average and the Joint Registration and Approximation of Structures in Images , 2003, International Journal of Computer Vision.

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

[10]  Stefano Soatto,et al.  Tracking deformable moving objects under severe occlusions , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

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

[12]  Chandra Kambhamettu,et al.  A Coarse-to-Fine Deformable Contour Optimization Framework , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Peihua Li,et al.  Visual contour tracking based on particle filters , 2003, Image Vis. Comput..

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

[15]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[16]  Sudeep Sarkar,et al.  The gait identification challenge problem: data sets and baseline algorithm , 2002, Object recognition supported by user interaction for service robots.

[17]  Larry S. Davis,et al.  Probabilistic template based pedestrian detection in infrared videos , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

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

[19]  James M. Coughlan,et al.  Finding Deformable Shapes Using Loopy Belief Propagation , 2002, ECCV.

[20]  Nikos Paragios,et al.  Shape Priors for Level Set Representations , 2002, ECCV.

[21]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[22]  G. Sapiro,et al.  Geometric partial differential equations and image analysis [Book Reviews] , 2001, IEEE Transactions on Medical Imaging.

[23]  Rachid Deriche,et al.  A PDE-based level-set approach for detection and tracking of moving objects , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[24]  Timothy F. Cootes,et al.  Combining point distribution models with shape models based on finite element analysis , 1994, Image Vis. Comput..

[25]  Frederic Fol Leymarie,et al.  Tracking Deformable Objects in the Plane Using an Active Contour Model , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Dimitris N. Metaxas,et al.  Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Gang Xu,et al.  A robust active contour model with insensitive parameters , 1993, 1993 (4th) International Conference on Computer Vision.

[28]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[29]  Q. Zheng,et al.  A computational vision approach to image registration , 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.

[30]  Timothy F. Cootes,et al.  Active shape models , 1998 .

[31]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..