Online Learning of Discriminative Correlation Filter Bank for Visual Tracking

Accurate visual tracking is a challenging research topic in the field of computer vision. The challenge emanates from various issues, such as target deformation, background clutter, scale variations, and occlusion. In this setting, discriminative correlation filter (DCF)-based trackers have demonstrated excellent performance in terms of speed. However, existing correlation filter-based trackers cannot handle major changes in appearance due to severe occlusions, which eventually result in the development of a bounding box for target drift tracking. In this study, we use a set of DCFs called discriminative correlation filter bank (DCFB) for visual tracking to address the key causes of object occlusion and drift in a tracking-by-detection framework. In this work, we treat thxe current location of the target frame as the center, extract several samples around the target, and perform online learning of DCFB. The sliding window then extracts numerous samples within a large radius of the area where the object in the next frame is previously located. These samples are used for the DCFB to perform correlation operation in the Fourier domain to estimate the location of the new object; the coordinates of the largest correlation scores indicate the position of the new target. The DCFB is updated according to the location of the new target. Experimental results on the quantitative and qualitative evaluations on the challenging benchmark sequences show that the proposed framework improves tracking performance compared with several state-of-the-art trackers