Learning temporal regularized spatial-aware deep correlation filter tracking via adaptive channel selection

In recent years, deep correlation filters have demonstrated outstanding performance in robust object tracking. Nevertheless, the correlation filters encounter challenges in managing huge occlusion, target deviation, and background clutter due to the lack of effective utilization of previous target information. To overcome these issues, we propose a novel temporal regularized spatial-aware deep correlation filter tracking via adaptive channel selection. To do this, we first presented the adaptive channel selection approach, which efficiently handles target deviation by adaptively selecting suitable channels during the learning stage. In addition, the adaptive channel selection method allows for dynamic adjustments to the filter based on the unique characteristics of the target object. This adaptability enhances the tracker's flexibility, making it well-suited for diverse tracking scenarios. Second, we propose the spatial-aware correlation filter with dynamic spatial constraints, which effectively reduces the filter response in the complex background region by distinguishing between the foreground and background regions in the response map. Hence, the target can be easily identified within the foreground region. Third, we designed a temporal regularization approach that improves the target accuracy when the case of large appearance variations. Additionally, this temporal regularization method considers the present and previous frames of the target region, which significantly enhances the tracking ability by utilizing historical information. Finally, we present a comprehensive experiments analysis of the OTB-2013, OTB-2015, TempleColor-128, UAV-123, UAVDT, and DTB-70 benchmark datasets to demonstrate the effectiveness of the proposed approach against the state-of-the-trackers.