The Velocity Snake: Deformable Contour for Tracking in Spatio-Velocity Space

We present a new active contour model for boundary tracking and position prediction of nonrigid objects, which results from applying a velocity control to the class of elastodynamical contour models, known as snakes. The proposed control term minimizes an energy dissipation function which measures the difference between the contour velocity and the apparent velocity of the image. Treating the image video-sequence as continuous measurements along time, it is shown that the proposed control results in robust tracking. This is in contrast to the original snake model which is proven to have tracking errors relative to image (object) velocity, thus resulting in high sensitivity to image clutter. The motion estimation further allows for position prediction of nonrigid boundaries. Based on the proposed control approach, we propose a new class of real time tracking contours, varying from models with batch-mode control estimation to models with real time adaptive controllers.

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

[2]  Anil K. Jain,et al.  Vehicle Segmentation and Classification Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

[5]  Andrew Blake,et al.  Statistical mosaics for tracking , 1996, Image Vis. Comput..

[6]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

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

[8]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  A. Feuer,et al.  Recursive optical flow estimation-adaptive filtering approach , 1996, Proceedings of 19th Convention of Electrical and Electronics Engineers in Israel.

[10]  Michael Elad,et al.  Recursive Optical Flow Estimation - Adaptive Filtering Approach , 1998, J. Vis. Commun. Image Represent..

[11]  Gilad Adiv,et al.  Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[13]  Jake K. Aggarwal,et al.  Articulated and elastic non-rigid motion: a review , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[14]  Shmuel Peleg,et al.  A Three-Frame Algorithm for Estimating Two-Component Image Motion , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  W. Clem Karl,et al.  Efficient multiscale regularization with applications to the computation of optical flow , 1994, IEEE Trans. Image Process..

[16]  Andrew Blake,et al.  Dynamic contours: real-time active splines , 1993 .

[17]  Harpreet S. Sawhney,et al.  Compact Representations of Videos Through Dominant and Multiple Motion Estimation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Gary J. Balas,et al.  Optical flow: a curve evolution approach , 1996, IEEE Trans. Image Process..

[19]  Natan Peterfreund,et al.  Robust tracking with spatio-velocity snakes: Kalman filtering approach , 1997, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).