Adaptive spatially regularized target attribute-aware background suppressed deep correlation filter for object tracking

In recent years, deep feature-based correlation filters have attained impressive performance in robust object tracking. However, deep feature-based correlation filters are affected by undesired boundary effects, which reduce the tracking performance. Moreover, the tracker moves towards a region that is identical to the target due to the sudden variation in target appearance and complicated background areas. To overcome these issues, we propose an adaptive spatially regularized target attribute-aware background suppressed deep correlation filter (ASTABSCF). To do this, a novel adaptive spatially regularized technique is presented, which aims to learn an efficient spatial weight for a particular object and fast target appearance variations. Specifically, we present a target-aware background suppression method with dual regression approach, which utilizes a saliency detection technique to produce the target mask. In this technique, we employ the global and target features to get the dual filters known as the global and target filters. Accordingly, global and target response maps are produced by dual filters, which are integrated into the detection stage to optimize the target response. In addition, a novel adaptive attribute-aware approach is presented to emphasize channel-specific discriminative features, which implements a post-processing technique on the observed spatial patterns to reduce the influence of less prominent channels. Therefore, the learned adaptive spatial attention patterns significantly reduce the irrelevant information of multi-channel features and improve the tracker performance. Finally, we demonstrate the efficiency of the ASTABSCF approach against existing modern trackers using the OTB-2013, OTB-2015, TempleColor-128, UAV-123, LaSOT, and GOT-10K benchmark datasets.