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.