Robust Contour Tracking by Combining Region and Boundary Information

This paper presents a new object tracking model that systematically combines region and boundary features. Besides traditional region features (intensity/color and texture), we design a new boundary-based object detector for accurate and robust tracking in low-contrast and complex scenes, which usually appear in the commonly used monochrome surveillance systems. In our model, region feature-based energy terms are characterized by probability models, and boundary feature terms include edge and frame difference. With a new weighting term, a novel energy functional is proposed to systematically combine the region and boundary-based components, and it is minimized by a level set evolution equation. For an efficient computational cost, motion information is utilized for new frame level set initialization. Compared with region feature-based models, the experimental results show that the proposed model significantly improves the performance under different circumstances, especially for objects in low-contrast and complex environments.