Robust long-term correlation tracking with multiple models

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.