Matting-driven online learning of Hough forests for object tracking

Abstract

Accurate segmentation provides a useful contour constraint to alleviate drifting during online learning for tracking. Towards this end, we present a closed loop method for object tracking that links Hough forests and alpha matting via an effective back-projection scheme for patches. A novel hybrid-Houghforests-based method first estimates object location. Given the object location, the trimap of matting is then automatically generated from the patches backprojected from the Hough forests. Subsequently, an accurate contour of the object can be obtained based on a robust matting technique. Based on such an accurate contour, an update strategy is utilized to obtain reliably labeled samples to update the Hough forests to decrease the risk of model drift. Extensive comparisons on challenging sequences demonstrate the robustness and effectiveness of the proposed method.