Two-Stage Object Tracking Method Based on Kernel and Active Contour

摘要

This letter presents a two-stage object tracking method by combining a region-based method and a contour-based method. First, a kernel-based method is adopted to locate the object region. Then the diffusion snake is used to evolve the object contour in order to improve the tracking precision. In the first object localization stage, the initial target position is predicted and evaluated by the Kalman filter and the Bhattacharyya coefficient, respectively. In the contour evolution stage, the active contour is evolved on the basis of an object feature image generated with the color information in the initial object region. In the process of the evolution, similarities of the target region are compared to ensure that the object contour evolves in the right way. The comparison between our method and the kernel-based method demonstrates that our method can effectively cope with the severe deformation of object contour, so the tracking precision of our method is higher.