Multi-template Tracker Driven by Cache Manager Algorithm, Towards Multi-distractor Scenarios

Although the Siamese tracker has evolved rapidly in recent years, lacking discrimination has always been its biggest disadvantage, compared to the discriminative correlation filters (DCF) tracker. It is because mainstream Siamese trackers take only the initial frame as the template. In this paper, we present a new multi-template transformer-based tracker to harness the tracking target’s temporal features and distinguish them from several distractors. It introduces the cache algorithm to adaptively store templates and dynamically recommend the most appropriate templates for the tracker. Moreover, a multi-template integration method is proposed here, by designing a transformer block with learnable template embedding, to reduce the impact of drifts in dynamic templates. We conduct quantitative experiments on multi-distractor scenarios and general scenarios. The results show the proposed tracker outperforms state-of-the-art trackers in multi-distractor scenarios, and can still keep real-time speed. Codes and models are available on GitHub¹.