Object Tracking Using Deformable Templates

We propose a novel method for object tracking using prototype-based deformable template models. To track an object in an image sequence, we use a criterion which combines two terms: the deviation of the object shape from its shape in the previous frame, and the fidelity of the detected shape to the input image. Shape and gradient information are used to track the object. We have also used the consistency between corresponding object regions throughout the sequence to help in trading the object of interest. Inter-frame motion is also used to track the boundary of moving objects. We have applied the algorithm to a number of image sequences from different sources. The inherent structure in the deformable template, together with region, motion, and image gradient cues, make the algorithm relatively insensitive to the adverse effects of weak image features and moderate partial occlusion.

[1]  Tai Sing Lee,et al.  Region competition: unifying snakes, region growing, energy/Bayes/MDL for multi-band image segmentation , 1995, Proceedings of IEEE International Conference on Computer Vision.

[2]  Anil K. Jain,et al.  Object Matching Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[4]  Ioannis A. Kakadiaris,et al.  Model-Based Estimation of 3D Human Motion , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Demetri Terzopoulos,et al.  Constraints on Deformable Models: Recovering 3D Shape and Nonrigid Motion , 1988, Artif. Intell..

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

[7]  Yiannis Aloimonos,et al.  Active vision , 2004, International Journal of Computer Vision.

[8]  Dimitris N. Metaxas,et al.  The integration of optical flow and deformable models with applications to human face shape and motion estimation , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Ioannis A. Kakadiaris,et al.  Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Ulrich Kressel,et al.  Tracking non-rigid, moving objects based on color cluster flow , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Dimitris N. Metaxas,et al.  Deformable model-based face shape and motion estimation , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

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

[13]  Rachid Deriche,et al.  Tracking complex primitives in an image sequence , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[14]  Timothy F. Cootes,et al.  A unified approach to coding and interpreting face images , 1995, Proceedings of IEEE International Conference on Computer Vision.

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

[16]  Rachid Deriche,et al.  Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Charles Kervrann,et al.  Robust tracking of stochastic deformable models in long image sequences , 1994, Proceedings of 1st International Conference on Image Processing.

[18]  Nicholas Ayache,et al.  Medical image tracking , 1993 .

[19]  Paul W. Fieguth,et al.  Color-based tracking of heads and other mobile objects at video frame rates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Alan L. Yuille,et al.  Deformable templates , 1993 .

[21]  Michael Unser,et al.  A family of polynomial spline wavelet transforms , 1993, Signal Process..

[22]  Demetri Terzopoulos,et al.  Analysis and Synthesis of Facial Image Sequences Using Physical and Anatomical Models , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  José M. N. Leitão,et al.  Adaptive B-splines and boundary estimation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[25]  Rachid Deriche,et al.  Region tracking through image sequences , 1995, Proceedings of IEEE International Conference on Computer Vision.