Densely Connected Discriminative Correlation Filters for Visual Tracking

Abstract

Discriminative Correlation Filters (DCFs)-based approaches have recently achieved competitive performance in visual tracking. However, such conventional DCF-based trackers often lack the discriminative ability due to the shallow architecture. As a result, they can hardly tackle drastic appearance variations and easily drift when the target suffers heavy occlusions. To address this issue, a novel densely connected DCFs framework is proposed for visual tracking. We incorporate multiple nested DCFs into the deep learning architecture, and then train the compact network with the data-specific target. Specifically, feature maps and interim response maps are shared and reused throughout the whole network. By doing so, the implicit information carried out by each DCF is fully exploited to enhance the model representation ability during the tracking process. Moreover, a multiscale estimation scheme is developed to account for scale variations. Experimental results on the benchmarks demonstrate that the proposed approach achieves outstanding performance compared to the existing state-of-the-art trackers.