Two-Stage Object Tracking Method Based on Kernel and Active Contour

This letter presents a two-stage object tracking method by combining a region-based method and a contour-based method. First, a kernel-based method is adopted to locate the object region. Then the diffusion snake is used to evolve the object contour in order to improve the tracking precision. In the first object localization stage, the initial target position is predicted and evaluated by the Kalman filter and the Bhattacharyya coefficient, respectively. In the contour evolution stage, the active contour is evolved on the basis of an object feature image generated with the color information in the initial object region. In the process of the evolution, similarities of the target region are compared to ensure that the object contour evolves in the right way. The comparison between our method and the kernel-based method demonstrates that our method can effectively cope with the severe deformation of object contour, so the tracking precision of our method is higher.

[1]  Larry S. Davis,et al.  Efficient mean-shift tracking via a new similarity measure , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[2]  Namrata Vaswani,et al.  Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Huiyu Zhou,et al.  Object tracking using SIFT features and mean shift , 2009, Comput. Vis. Image Underst..

[4]  Rachid Deriche,et al.  Geodesic active regions and level set methods for motion estimation and tracking , 2005, Comput. Vis. Image Underst..

[5]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

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

[7]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[8]  Ralph Gross,et al.  Constructing and Fitting Active Appearance Models With Occlusion , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[9]  Pheng-Ann Heng,et al.  Parametric active contours for object tracking based on matching degree image of object contour points , 2008, Pattern Recognit. Lett..

[10]  Daijin Kim,et al.  A background robust active appearance model using active contour technique , 2007, Pattern Recognit..

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

[12]  Thomas Kalinke,et al.  Computer vision for driver assistance systems , 1998, Defense, Security, and Sensing.

[13]  D. Salmond,et al.  Target tracking: introduction and Kalman tracking filters , 2001 .

[14]  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..

[15]  Natan Peterfreund,et al.  Robust Tracking of Position and Velocity With Kalman Snakes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Yongmin Kim,et al.  Semiautomatic video object segmentation using VSnakes , 2003, IEEE Trans. Circuits Syst. Video Technol..

[17]  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.

[18]  Richard Szeliski,et al.  A layered video object coding system using sprite and affine motion model , 1997, IEEE Trans. Circuits Syst. Video Technol..

[19]  Gary Bradski,et al.  Computer Vision Face Tracking For Use in a Perceptual User Interface , 1998 .

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

[21]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

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

[23]  Rashid Ansari,et al.  Kernel particle filter for visual tracking , 2005, IEEE Signal Processing Letters.

[24]  H. H. Chen,et al.  Video Object Extraction via MRF-Based Contour Tracking , 2010, IEEE Transactions on Circuits and Systems for Video Technology.