Robust visual tracking algorithm with coattention guided Siamese network

Abstract

In recent years, Siamese trackers locate the target based on the cross-correlation results of the template and the search branch. Siamese trackers emphasized better features embedding and similarity matching. The latest work focuses on enriching the feature representation of the template. However, it is still difficult for the tracker to eliminate the side effect of the distractors in the search region, resulting in the decline in tracking accuracy. We propose a coattention guided Siamese network (CGS) for visual tracking from two aspects. To mine the inherent correlation between the template and search branch, a coattention block is proposed that can guide the target-aware feature learning during the tracking process. To wisely choose the target-specific features, a self-gated mechanism is exploited on the template features, which can adaptively reallocate the confidence of channels and strengthen the feature representation. We conduct extensive experiments on OTB2015, VOT2016, and GOT-10K benchmarks to evaluate the performance of the proposed method. Experimental results demonstrate that our CGS network performs favorable against other state-of-the-art methods. Moreover, our CGS runs at 79FPS, far above the real-time requirement.