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Deep Siamese trackers have recently gained much attention in recent yearssince they can track visual objects at high speeds. Additionally, adaptivetracking methods, where target samples collected by the tracker are employedfor online learning, have achieved state-of-the-art accuracy. However, singleobject tracking (SOT) remains a challenging task in real-world application dueto changes and deformations in a target object's appearance. Learning on allthe collected samples may lead to catastrophic forgetting, and thereby corruptthe tracking model.  In this paper, SOT is formulated as an online incremental learning problem. Anew method is proposed for dynamic sample selection and memory replay,preventing template corruption. In particular, we propose a change detectionmechanism to detect gradual changes in object appearance and select thecorresponding samples for online adaption. In addition, an entropy-based sampleselection strategy is introduced to maintain a diversified auxiliary buffer formemory replay. Our proposed method can be integrated into any object trackingalgorithm that leverages online learning for model adaptation.  Extensive experiments conducted on the OTB-100, LaSOT, UAV123, andTrackingNet datasets highlight the cost-effectiveness of our method, along withthe contribution of its key components. Results indicate that integrating ourproposed method into state-of-art adaptive Siamese trackers can increase thepotential benefits of a template update strategy, and significantly improveperformance.

Dynamic Template Selection Through Change Detection for Adaptive Siamese Tracking