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

Computer Vision and Pattern Recognition