Adaptive Objectness for Object Tracking

To exploit the reliable prior knowledge that the target object in tracking must be an object other than nonobject, in this letter, we propose to adapt objectness for visual object tracking. Instead of directly applying an existing objectness measure that is generic and handles various objects and environments, we adapt it to be compatible to the specific tracking sequence and object. More specifically, we use the newly proposed binarized normed gradient (BING) objectness as the base, and then train an object-adaptive objectness for each tracking task. The training is implemented by using an adaptive support vector machine that integrates information from the specific tracking target into the BING measure. We emphasize that the benefit of the proposed adaptive objectness, named ADOBING, is generic. To show this, we combine ADOBING with eight top performed trackers in recent evaluations. We run the ADOBING-enhanced trackers along with their base trackers on the CVPR2013 benchmark, and our methods consistently improve the base trackers both in overall performance and under all challenge factors. Noting that the way we integrate objectness in visual tracking is generic and straightforward, we expect even more improvement by using tracker-specific objectness.