Contour tracking via on-line discriminative active contours

Abstract

This paper presents a novel on-line AdaBoost based discriminative active contour tracking framework (ADACT) using level sets. First we build an on-line AdaBoost based appearance model to track and extract the rough target region, which provides important discriminative clues for our active contour model. Integrating with both edge and discriminative region information, a new active contour model is proposed for obtaining accurate target contour after curve evolution. Experiments on the challenging video sequences demonstrate that the proposed method can achieve more robust deformable target contour tracking under various situations than other competitive contour tracking methods.