Robust Multi-Model Visual Tracking With Distractor-Aware Template-Coupled Correlation Filters Joint Learning

Existing correlation filter (CF) tracking methods are fragile for boundary effects, vague target information, and heuristic model updating, as these limitations degrade the detection ability of the learned filter. In response to that, this article embarks on basic CF learning and presents a novel distractor-aware template-coupled correlation filter (DATC-CF) by exploiting the spatial-temporal appearance context of the target, which aims at improving the discriminative ability of the learned filter against distractive background and the descriptive ability in adapting unexpected scenes. Specifically, the power of spatial context comes from a distractor-aware regularizer weighted by background distractors. By adaptively optimizing the weight of each distractor, our filter training can focus more on the critical distractors. The temporal context is represented by a dynamic template set, and we formulate a template-coupled regularizer that can make use of the commonality over all templates while maintaining a passive filter update under a multi-template learning scheme. DATC-CF integrates the two regularizers and is summarized as a multi-variable joint optimization problem where a filter ensemble can be learned. With DATC-CF, a multi-model tracking framework DATC_MM is developed by maximizing the posterior distribution over the learned filters. For robust tracking, we further apply high-confidence updating and establish a complementary distractor-aware color detector to restore the CF tracking failures. Finally, experiments on several large-scale benchmark datasets demonstrate the effectiveness of the proposed tracking methods against state-of-the-art trackers.