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.

To address the challenge of repetitive target appearance variation and frequent occlusion, existing visual tracking methods either handle corrupted samples or correct the appearance model. In this study, the authors propose a novel framework that successfully combines these two strategies. In their method, the base tracker is an improved discriminative correlation filter‐based tracker, in which an independent classifier is employed to alleviate the problem of corrupted samples; the best model is selected for improvement from a group of models, which they call a ‘model colony’. The model colony is composed of models updated via different processes. The correlation output and the peak‐to‐sidelobe ratio are used to evaluate each model in the model colony. In addition, they propose a novel criterion called the maximum‐to‐others ratio for superior model selection. Experiments on 80 challenging sequences show that their tracker outperforms state‐of‐the‐art trackers. In addition, experimental results demonstrate that their formulation significantly improves the performance of their base tracker.

Visual object tracking is an important and challenging task in computer vision. In this study, the authors propose a novel visual tracking approach by decomposing the tracking task into translation and scale estimation. In translation estimation, they employ multiple adaptive correlation filters with features of hierarchical convolutional neural networks (CNNs) to more accurately estimate the target location. To make full use of multi‐level features from different CNN layers, they propose an adaptive weighted algorithm to fuse correlation response maps. In scale estimation, a one‐dimensional correlation filter with histogram of oriented gradient (HOG) features is employed to estimate the scale variation. Extensive experimental results on 50 challenging benchmark video sequences demonstrate that the proposed algorithm outperforms state‐of‐the‐art algorithms.