Background-Aware Correlation Filter for Object Tracking with Deep CNN Features

Correlation filter tracking algorithms have garnered significant attention due to their efficiency and outstanding tracking performance. However, these methods face several limitations. Firstly, they rely on periodic boundary conditions, leading to boundary effects. Secondly, most traditional methods only extract hand-crafted features from the image. Nevertheless, these features are insufficient to discriminate in complex scenes. Thirdly, they assume that the maximum position of the correlation response represents the object without further evaluating its reliability. These limitations make it very easy to lose the object in occluded scenes. A background-aware correlation filter algorithm based on an anti-occlusion mechanism and deep features is proposed to solve the above limitations. Primarily, the video frames to be processed are cyclically shifted to crop the cyclically shifted samples in a small window. This operation significantly reduces boundary effects while obtaining background samples from the real world. Then, deep features are extracted through deep neural networks. Subsequently, we propose a background perception tracking framework that synchronously estimates position and scale based on these features. This framework aims to determine the optimal candidate sample position and scale. Finally, an anti-occlusion mechanism is constructed to evaluate the optimal candidate samples obtained in each frame further. This mechanism fully exploits the diversity of objects and effectively solves the tracking drift and failure issues caused by occlusion, fast motion, and so on. Extensive experiments are conducted on the object tracking benchmark (OTB) dataset and compared with industry-leading tracking algorithms to validate the effectiveness of the proposed method. The results show that the method has robust tracking performance.