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Holistic object representation-based trackers suffer from performance dropunder large appearance change such as deformation and occlusion. In this work,we propose a dynamic part-based tracker and constantly update the target partrepresentation to adapt to object appearance change. Moreover, we design anattention-guided part localization network to directly predict the target partlocations, and determine the final bounding box with the distribution of targetparts. Our proposed tracker achieves promising results on various benchmarks:VOT2018, OTB100 and GOT-10k

TAPL: Dynamic Part-based Visual Tracking via Attention-guided Part Localization