Augmenting cascaded correlation filters with spatial-temporal saliency for visual tracking

We herein propose a novel visual tracking approach using cascaded discriminative correlation filters (DCFs). The approach consists of two stages. In the first stage, a DCF is trained with high-level convolutional features to initially estimate the location of the object. In the second stage, another DCF is trained using low-level convolutional features to refine the object location. To efficiently track the deformable or occluded objects, spatial-temporal saliency is introduced to enhance the second stage DCF. The proposed approach is tested on the VOT2015 and OTB-13 benchmark datasets. The experimental results show that our tracker achieves state-of-the-art performance and performs extremely well in tracking nonrigid, fast moving, or occluded objects.