  While recent years have witnessed remarkable progress in the feature
representation of visual tracking, the problem of feature misalignment between
the classification and regression tasks is largely overlooked. The approaches
of feature extraction make no difference for these two tasks in most of
advanced trackers. We argue that the performance gain of visual tracking is
limited since features extracted from the salient area provide more
recognizable visual patterns for classification, while these around the
boundaries contribute to accurately estimating the target state.
  We address this problem by proposing two customized feature extractors, named
polar pooling and extreme pooling to capture task-specific visual patterns.
Polar pooling plays the role of enriching information collected from the
semantic keypoints for stronger classification, while extreme pooling
facilitates explicit visual patterns of the object boundary for accurate target
state estimation. We demonstrate the effectiveness of the task-specific feature
representation by integrating it into the recent and advanced tracker RPT.
Extensive experiments on several benchmarks show that our Customized Features
based RPT (RPT++) achieves new state-of-the-art performances on OTB-100,
VOT2018, VOT2019, GOT-10k, TrackingNet and LaSOT.
