Wide-Area Search Tracking for Siamese Region Proposal Network

With the introduction of deep learning technology into the field of visual tracking, the accuracy and robustness of visual tracking have greatly improved. Recently, trackers based on Siamese region proposal networks have attracted a great deal of attention because of their favorable performance, especially when faced with challenges of heavy target deformation and out-of-plane rotation. However, strategies of local-area search and using a fixed template degrade the performance of such networks in the long-term tracking. In this paper, we focus on a wide-area search tracking approach with adaptive template updating for a Siamese region proposal network. By embedding a correlation filter module into the Siamese region proposal networks, a structure of moving anchors distribution is designed to make the anchor regression centered around the target. Through adaptive reliability evaluation and online template update, the discriminative performance of the model improves greatly. Furthermore, in order to address the problem of being misled by false positives, a multi-trajectory tracking mechanism is joined to consistently boost the classification ability. Experiments on OTB100 and VOT short-term benchmarks and UAV123 long-term benchmark show that our tracker significantly outperforms the original algorithm and achieves comparable performance compared with other state-of-the-art trackers.