An active contour tracking method by matching foreground and background simultaneously

This paper presents a novel active contour tracking method, which is used to estimate the non-grid deformation of the motion target and can get the accurate contour of the tracked target. In the proposed method, level set is employed to represent the target region. By using Bhattacharyya similarity as a metric, the proposed method aims at finding a best candidate region in the video frame, whose foreground distribution and background distribution match maximally those of the predefined tracked target. Based on this metric, we derive a level set based object tracking formulation, which estimates iteratively the contour change of the target. Experimental results on the representative video sequences show that the proposed methods perform better than EM-shift and SOAMST algorithm.

[1]  D. Zhang,et al.  Scale and orientation adaptive mean shift tracking , 2012 .

[2]  Robert T. Collins,et al.  Mean-shift blob tracking through scale space , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

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

[4]  Alper Yilmaz,et al.  Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Larry S. Davis,et al.  Mean-shift analysis using quasiNewton methods , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[6]  R. Schreiber Numerical Methods for Partial Differential Equations , 1999 .

[7]  Ben J. A. Kröse,et al.  An EM-like algorithm for color-histogram-based object tracking , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[8]  Ming-Hsuan Yang,et al.  Robust Object Tracking with Online Multiple Instance Learning , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Tao Zhang,et al.  Improving performance of distribution tracking through background mismatch , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Tao Zhang,et al.  Active contours for tracking distributions , 2004, IEEE Transactions on Image Processing.

[11]  Pierre Kornprobst,et al.  Mathematical problems in image processing - partial differential equations and the calculus of variations , 2010, Applied mathematical sciences.

[12]  Lei Zhang,et al.  Active contours with selective local or global segmentation: A new formulation and level set method , 2010, Image Vis. Comput..

[13]  Shai Avidan,et al.  Ensemble Tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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