Robust Context-Aware Tracking with Temporal Regularization

Discriminative Correlation Filters demonstrate superior capabilities, while still suffering from background clutter. The proposed context-aware correlation filter (CACF) framework effectively avoids the interference of background noise with the explicit incorporation of global context information. However, there is still sequential context information that is not considered. This work proposes a robust context-aware tracking based on hand-crafted features by adding a temporal regularization. The temporal regularization term provides temporal information for learning filter, which limits the mutation of the filter. Experiments on OTB-100 show that our tracker demonstrates excellent accuracy and significantly improves the robustness of CF trackers and those trackers in the CACF framework.