Robust object tracking and re-detection require stably predicting the trajectory of the target object and recovering from tracking failure by quickly re-detecting it when it is lost during long-term tracking. The locations of the target and the background are calculated relative to the region occupied by the object. The effect of tracking can be enhanced by isolating the target and the background, modeling and tracking them, respectively, and integrating their tracking results. In this study, we propose an approach that builds motion models for the target and its context. Tracking results from a target tracker and a context tracker are integrated through linear fusion to predict the position of the target. A kernelized correlation filter (KCF) tracker is used to track the target in the predicted position. When the target is lost, it can be quickly recovered by searching in the given field of view using a target model built and updated through observation models that are constructed prior to the loss of the target. Our approach is not sensitive to the segmentation of the target and the context. The motion models and observation models of the target and the context work together in the tracking process, whereas the target model alone is involved in re-detection. Experiments to test our proposed approach, which simultaneously models the target and its context, showed that it can effectively enhance the robustness of long-term tracking.

Multi-channel discriminative correlation filter (DCF) tracking methods have exhibited superior performance on several benchmarks. However, existing methods usually treat each channel of the features equally, whereas they pay less attention to the contribution of different channels. Different channels exhibit variant properties in the tracking process. A DCF learned with equally important channels is likely to be contaminated by the unreliable ones, which results in model degradation. To address this problem, we propose a new formulation for jointly learning the channel reliability and the correlation filters. The formulation is generic, and it can be combined with existing techniques in the DCF framework to further improve the performance. Our method can adaptively increase the impact of reliable channels and down-weight the corrupted ones. To solve the joint learning problem, we propose an optimization strategy that alternates between the correlation filters and the channel weights. Further, we prove the upper bound of the objective function and solve the channel weights efficiently. The joint learning strategy makes the correlation filters more discriminative and the channel weights more accurate. To verify the joint formulation, we propose a tracker based on the proposed formulation and the techniques used in the ECO tracker. We conduct extensive experiments to evaluate the proposed tracker on three benchmarks. The experimental results show that our formulation is effective and efficient, and that it performs favorably against other state-of-the-art trackers.