A simple and efficient template matching algorithm

We propose a general framework for object tracking in video images. It consists of low-order parametric models for the image motion of a target region. These models are used to predict the movement and to track the target. The difference of intensity between the pixels belonging to the current region and the pixels of the selected target (learnt during an off-line stage) allows a straightforward prediction of the region position in the current image. The proposed algorithm allows to track in real time (less than 10 ms) any planar textured target under homographic motions. This algorithm is very simple (a few lines of code) and very efficient (less than 10 ms on a 150 MHz hardware).

[1]  Filiberto Pla,et al.  Matching Feature Points in Image Sequences through a Region-Based Method , 1997, Comput. Vis. Image Underst..

[2]  A. Pentland,et al.  Real time tracking and modeling of faces: an EKF-based analysis by synthesis approach , 1999, Proceedings IEEE International Workshop on Modelling People. MPeople'99.

[3]  Frédéric Jurie,et al.  Solution of the Simultaneous Pose and Correspondence Problem Using Gaussian Error Model , 1999, Comput. Vis. Image Underst..

[4]  Alex Pentland,et al.  Space-time gestures , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

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

[6]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[7]  Hans-Hellmut Nagel,et al.  3D Pose Estimation by Directly Matching Polyhedral Models to Gray Value Gradients , 1997, International Journal of Computer Vision.

[8]  G.-S. Young,et al.  3-D motion estimation using a sequence of noisy stereo images , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  James L. Crowley,et al.  Measuring Image Flow By Tracking Edge-lines , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[10]  Alex Pentland,et al.  Task-Specific Gesture Analysis in Real-Time Using Interpolated Views , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

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

[13]  Alex Pentland,et al.  A subspace method for maximum likelihood target detection , 1995, Proceedings., International Conference on Image Processing.

[14]  David G. Lowe,et al.  Robust model-based motion tracking through the integration of search and estimation , 1992, International Journal of Computer Vision.

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

[16]  Alex Pentland,et al.  A three-dimensional model of human lip motions trained from video , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

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

[18]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[19]  Roberto Brunelli,et al.  MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES , 2001 .