  Object tracking is a long standing problem in vision. While great efforts
have been spent to improve tracking performance, a simple yet reliable prior
knowledge is left unexploited: the target object in tracking must be an object
other than non-object. The recently proposed and popularized objectness measure
provides a natural way to model such prior in visual tracking. Thus motivated,
in this paper 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 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 seven top performed trackers in recent evaluations. We run
the ADOBING-enhanced trackers with their base trackers on two popular
benchmarks, the CVPR2013 benchmark (50 sequences) and the Princeton Tracking
Benchmark (100 sequences). On both benchmarks, our methods not only
consistently improve the base trackers, but also achieve the best known
performances. Noting that the way we integrate objectness in visual tracking is
generic and straightforward, we expect even more improvement by using
tracker-specific objectness.
