All-day and Real-time Multi-regularized Correlation Filter for UAV Object Tracking

Correlation filter (CF) has been widely used in unmanned aerial vehicle (UAV) object tracking. Due to the computational limitation of the UAV platform, the existing UAV tracking algorithms rely heavily on the handcrafted features, which cannot obtain all the semantic information of the target. Meanwhile, the existing tracking algorithms focus on the tracking of daytime targets, and ignore the tracking problem of nighttime targets. In addition, when the correlation filter-based tracker uses the cosine window to suppress the boundary effect caused by the cyclic shift, the sampling area is shrunk, which causes the contamination of training samples and inevitably deteriorates the performance of the tracker. Aiming at the above problems, we propose an all-day and real-time multi-regularized correlation filter (AMRCF) for UAV object tracking. Firstly, an adaptive image enhancement module is introduced to enhance the image without affecting its color ratio of each channel and improve the nighttime tracking performance. Secondly, a lightweight deep network is introduced to extract the deep features of the target and represent the semantic information together with the hand-crafted features. Thirdly, a Gaussian-shaped mask is embedded in the correlation filter framework, which can effectively avoid the contamination of training samples while suppressing the boundary effect. Finally, extensive experiments are conducted on five publicly available UAV benchmark datasets. The experimental results show that the proposed algorithm achieves competitive result compared with several state-of-the-art correlation filter-based trackers, and the real-time speed is about 25 fps, which makes it competent for UAV object tracking task. © 2023 Science Press. All rights reserved.