Channel Attentional Correlation Filters Learning With Second-Order Difference for UAV Tracking
Yang Zhang, Yu‐Feng Yu, Ke-Kun Huang, Yingxu Wang · IEEE Geoscience and Remote Sensing Letters · 2023
Unmanned aerial vehicle (UAV) visual tracking has been a hot research topic in the field of remote sensing. Many filter-based UAV trackers have achieved excellent performance. However, existing methods do not distinguish the importance of different feature channels with semantic information and background information, which may hinder the tracker’s ability to adapt to changing environments. To deal with this problem, we propose a channel attentional correlation filters learning model (CACF). Specifically, we introduce the fuzzy C-means algorithm to pre-classify the extracted features and then perform weight penalty to feature channels with different membership degrees. In addition, the filter can adapt more effectively to the background’s rapid changes during the UAV tracking process by learning the second-order difference between adjacent three frame features. Finally, the comparative experiments are conducted on three mainstream UAV datasets, including DTB70, UAV123@10fps, and UAVDT. The experimental results demonstrate the effectiveness of the proposed method. The tracking performance of CACF surpasses that of other state-of-the-art trackers.