Exploiting structural constraints for visual object tracking

摘要

This paper presents a novel structure-aware method for visual tracking. The proposed tracker relies on keypoint regions as salient and stable elements that encode the object structure efficiently. In addition to the object structural properties, the appearance model also includes global color features that we first use in a probabilistic approach to reduce the search space. The second step of our tracking procedure is based on keypoint matching to provide a preliminary prediction of the target state. Final prediction is then achieved by exploiting object structural constraints, where target keypoints vote for the corrected object location. Once the object location is obtained, we update the appearance model and structural properties, allowing tracking targets with changing appearance and non-rigid structures. Extensive experiments demonstrate that the proposed Structure-Aware Tracker (SAT) outperforms recent state-of-the-art trackers in challenging scenarios, especially when the target is partly occluded and in moderately crowded scenes.