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. (C) 2018 Elsevier Inc. All rights reserved.