Learning Dynamic-Sensitivity Enhanced Correlation Filter With Adaptive Second-Order Difference Spatial Regularization for UAV Tracking

Yu-Feng Yu, Zhongsen Chen, Yang Zhang, Chuanbin Zhang, Weiping Ding

Abstract: Discriminative correlation filter (DCF)-based tracking algorithms continue to advance in the field of UAV tracking due to their computational efficiency. The idea of integrating the advantages of historical information and response adjustments into the CF tracking framework is continuously being developed. However, maintaining the stability of mobile video tracking in highly dynamic environments is extremely challenging. This difficulty arises from frequent changes in targets and backgrounds, as well as the stochastic noise generated by the photon-counting process in sensors. In addition, the inconsistent rates of these changes are often overlooked and require further scrutiny. In this paper, we propose a dynamic sensitivity enhanced correlation filter with adaptive second-order difference spatial regularization to address the issue of inconsistent motion rates in dynamic videos. We use the non-local means algorithm to denoise template images before feature extraction, improving the discriminative power of target contours. Then, we incorporate the proposed dynamic-sensitivity error method into CF learning and employ a novel adaptive second-order difference spatial regularization to simultaneously optimize the filter coefficients and spatial regularization weights. This regularization effectively works in synergy with the dynamic-sensitivity error strategy. Furthermore, an additional ADMM optimizer is introduced to derive the solution, thereby improving the convergence and computational efficiency of the algorithm. This algorithm supports the adjustment of filter updates in dynamic environments by balancing consistency with previous filter templates and flexibility to accommodate rapid target changes. By conducting extensive experiments on three challenging UAV tracking databases, we compare the proposed model with existing models. The experimental results demonstrate our superior performance. Code is released at: https://github.com/Johnsonirene/LDECF.