Contour tracking via on-line discriminative appearance modeling based level sets

Abstract

A novel level set method based on on-line discriminative appearance modeling (DAMLSM) is presented for contour tracking. In contrast with traditional level set models which emphasize the intensity consistent segmentation and consider no priors, the proposed DAMLSM takes the context of tracking into account and use a discriminative patch based target model to guide the curve evolution. By modeling both the region and edge cues in a Bayesian manner, the proposed level set method can lead an accurate convergence to the candidate region with maximum likelihood of being the target. Finally, we update the target model to adapt to the appearance variation, enabling tracking to continue under occlusion. Experiments confirm the robustness and reliability of our method.