Object discriminability re-extraction for distractor-aware visual object tracking

Abstract

The similar distractor problem is one of the most difficult challenges for Siamese-based trackers. Since they formulate the visual tracking task as a similar matching problem, these trackers involve an essential problem that they are sensitive to the intra-class and inter-class instances with similar appearance confusion. To solve the problem, we propose an object discriminability re-extraction network (ODR-Net) for distractor-aware visual object tracking. The network first mines similar distractors from existing tracking information with a distractor capture module, and then re-extracts discriminative features to redetect the target from distractors with a discriminative feature re-extraction module. It solves the distractor problem in the decoding phase of a tracker and can be considered as a general block that applied to existing Siamese trackers to tackle the similar distractor problem. To demonstrate the effectiveness of the proposed method, extensive experiments and comparisons with state-of-the-art trackers are conducted on a variety of large-scale benchmark datasets, including GOT-10k, LaSOT, OTB-2015, TrackingNet, VOT2020, VOT2021, and VOT2022. Without bells and whistles, our ODR-Net achieves leading performance with a real-time speed.