Object tracking using adaptive block matching

We propose an object-tracking algorithm that predicts the object contour using motion vector information. Tracking is achieved by predicting the object boundary using motion vectors, followed by contour update, using occlusions/disocclusion detection. An adaptive block-based approach has been used for estimating motion between frames. An efficient modulation scheme is used to control the gap between frames used for object tracking. The algorithm for detecting occlusions proceeds in two steps. First, covered regions are estimated from the displaced frame difference. These covered regions are classified into actual occlusions and false alarms using motion characteristics. Disocclusion detection is also performed in a similar manner. The immediate applications of the proposed tracking algorithm are video compression using MPEG-4 and content retrieval based on standards like H.26L. Preliminary simulation results demonstrate the performance of the proposed algorithm.

[1]  William A. Barrett,et al.  Interactive Segmentation with Intelligent Scissors , 1998, Graph. Model. Image Process..

[2]  Edward H. Adelson,et al.  Representing moving images with layers , 1994, IEEE Trans. Image Process..

[3]  Daniel Gatica-Perez,et al.  Multiview extensive partition operators for semantic video object extraction , 2001, IEEE Trans. Circuits Syst. Video Technol..

[4]  P. Strevens.  Iii , 1985 .

[5]  Daniel Gatica-Perez,et al.  Semantic video object extraction using four-band watershed and partition lattice operators , 2001, IEEE Trans. Circuits Syst. Video Technol..

[6]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[7]  A. Murat Tekalp,et al.  Occlusion-adaptive, content-based mesh design and forward tracking , 1997, IEEE Trans. Image Process..

[8]  Chung-Lin Huang,et al.  A new motion compensation method for image sequence coding using hierarchical grid interpolation , 1994, IEEE Trans. Circuits Syst. Video Technol..

[9]  Montse Pardàs,et al.  Hierarchical morphological segmentation for image sequence coding , 1994, IEEE Trans. Image Process..

[10]  Dan Schonfeld,et al.  Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream , 2003, IEEE Trans. Multim..

[11]  Natan Peterfreund.  The velocity snake , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[12]  Dan Schonfeld,et al.  VORTEX: Video Retrieval and Tracking from Compressed Multimedia Databases - Multiple Object Tracking from MPEG-2 Bit Stream , 2000, J. Vis. Commun. Image Represent..

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

[14]  A. Murat Tekalp,et al.  Tracking visible boundary of objects using occlusion adaptive motion snake , 2000, IEEE Trans. Image Process..

[15]  Ramesh C. Jain,et al.  Using Dynamic Programming for Solving Variational Problems in Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Bilge Gunsel,et al.  Two-dimensional mesh-based visual-object representation for interactive synthetic/natural digital video , 1998, Proc. IEEE.

[17]  Arch C. Luther.  Video technology , 1994 .

[18]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, International Journal of Computer Vision.

[19]  Robert C. Kohberger,et al.  Cluster Analysis (3rd ed.) , 1994 .

[20]  Luis Torres,et al.  Region-based video coding using mathematical morphology , 1995 .

[21]  Richard Szeliski,et al.  Tracking with Kalman snakes , 1993 .