Adjustable Tracking Algorithm with Adaptive Template Matching

This paper presents an adjustable algorithm for object tracking in a sequence of images. It is a method designed to be effective in applications where some of the information on the object tracked is known. To establish the object position and size in a frame, adaptive template matching, location prediction through known trajectory, border information in the image, and data acquired in previous frames are used. This algorithm behaves nicely with sequences of images where the tracked object size and features vary considerably. It is also robust to some levels of occlusion, and it can be adapted to simpler applications to compete in speed with a basic correlation algorithm.

[1]  Takahiro Ishikawa,et al.  The template update problem , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Tomaso A. Poggio,et al.  A bootstrapping algorithm for learning linear models of object classes , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Barry R. Masters,et al.  Digital Image Processing, Third Edition , 2009 .

[4]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[6]  Anil K. Jain,et al.  Object tracking using deformable templates , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[7]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[8]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[10]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

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

[12]  Jing Xiao,et al.  Robust full-motion recovery of head by dynamic templates and re-registration techniques , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[13]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.

[14]  Gregory D. Hager,et al.  Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998, IEEE Trans. Pattern Anal. Mach. Intell..