Evidence-based object tracking via global energy maximization

This paper describes a robust algorithm for arbitrary object tracking in long image sequences. This technique extends the dynamic Hough transform proposed in our earlier work to detect arbitrary shapes undergoing affine motion. The proposed tracking algorithm processes the whole image sequence globally. First, the object boundary is represented in lookup-table form, and we then perform an operation that estimates the energy of the motion trajectory in the parameter space. We assign an extra term in our cost function to incorporate smoothness of deformation. The object is actually rigid, so by 'deformation' we mean changes due to rotation or scaling of the object. There is no need for training or initialization, and an efficient implementation can be achieved with coarse-to-fine dynamic programming and pruning. The method, because of its evidence-based nature, is robust under noise and occlusion.