Shape Tracking Using Centroid-Based Methods
Algorithms for tracking generic 2D object boundaries in a video sequence should not make strong assumptions about the shapes to be tracked. When only a weak prior is at hand, the tracker performance becomes heavily dependent on its ability to detect image features; to classify them as informative (i.e., belonging to the object boundary) or as outliers; and to match the informative features with corresponding model points. Unlike simpler approaches often adopted in tracking problems, this work looks at feature classification and matching as two unsu-pervised learning problems. Consequently, object tracking is converted into a problem of dynamic clustering of data, which is solved using competitive learning algorithms. It is shown that competitive learning is a key technique for obtaining accurate local motion estimates (avoiding aperture problems) and for discarding the outliers. In fact, the competitive learning approach shows several benefits: (i) a gradual propagation of shape information across the model; (ii) the use of shape and noise models competing for explaining the data; and (iii) the possibility of adopting high dimensional feature spaces containing relevant information extracted from the image. This work extends the unified framework proposed by the authors in .
KeywordsObject Boundary Model Point Model Sample Edge Point Active Contour Model
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- 3.Y. Bar-Shalom and T. Fortmann. Tracking and Data Association. Academic Press, 1988.Google Scholar
- 5.J. Bioucas. Adaptive Bayesian contour estimation: A vector space representation approach. In Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, pages 157–172, York, UK, 1999.Google Scholar
- 6.A. Blake and M. Isard. Active Contours. Springer-Verlag, 1998.Google Scholar
- 13.J. Marques and J. Lemos. Optimal and suboptimal shape tracking based on multiple switched dynamic models. Image and Vision Computing, 2001.Google Scholar
- 14.X. Pardo and P. Radeva. Discriminant snakes for 3D reconstruction in medical images. In Proc. Int. Conf. on Pattern Recognition, volume 4, pages 336–339, Barcelona, 2000.Google Scholar
- 16.H. Tagare. Deformable 2-D template matching using orthogonal curves. Trans. Medical Imaging, pages 108–117, 1997.Google Scholar