Recently, by virtue of the high computational efficiency and accuracy, discriminative correlation filter (DCF)- based tracking methods have gained attraction in the field of unmanned aerial vehicle (UAV). However, conventional DCF-based methods merely rely on cyclic shift to produce training samples. As a result, the filter trained by these samples owns limited discriminative ability, ineffectively addressing various challenges in the tracking stage. Here, to promote the filter's discriminative ability, we develop a feature block-aware correlation filter (CF) method. Specifically, the extracted feature is divided into two blocks, i.e., target and background feature blocks. These blocks only contain target and background features, respectively, by using different mask matrixes. Then, two regularization terms are proposed to combine both feature blocks into the DCF framework. In addition, we employ effective channel reliability weights to generate target response for precise positioning. Furthermore, substantial experiments have been accomplished on multiple public UAV benchmarks, proving that our tracker possesses superior tracking capabilities and operates at ∼40 frames per second (FPS) on the CPU platform.

According to Darwinian evolutionary theory, numerous species in the wild have developed remarkable adaptive mechanisms, involving pattern rearrangement and environmental assimilation, to evade predators. These obfuscation strategies pose significant challenges for both individuals and algorithms when performing the Camouflage Object Detection (COD) task in complex and intricate scenarios. Inspired by human strategies in the COD task, which involve assigning uncertainties to the entire input and then focusing on highly uncertain areas with the aid of prior knowledge such as boundary information, we propose the Uncertainty-Edge Dual Guide (UEDG) architecture. UEDG effectively combines probabilistic-derived uncertainty and deterministic-derived edge information to accurately detect concealed objects. The architecture consists of two independent branches dedicated to uncertainty reasoning and edge inference, which are subsequently integrated into a feature fusion module utilizing recursion feedback and feature-reuse techniques. This novel COD framework leverages the benefits of Bayesian learning and convolution-based learning, resulting in a powerful multi-task guided approach. Extensive experiments conducted on four widely employed datasets demonstrate the superior performance of UEDG compared to 12 state-of-the-art approaches, while maintaining an acceptable level of computational complexity. Overall, UEDG presents a promising solution for addressing the challenges of COD in complex environments by combining evolutionary-inspired strategies with advanced computer vision techniques.

With the high computation efficiency and tracking accuracy, discriminative correlation filters (DCF) have been applied to UAV tracking. However, in the scenarios (i.e., complex background and temporary occlusion), DCF-based trackers usually generate low credibility response under the influence of background distractors, which contains multiple side peaks and declines the tracking performance. Motivated by the response consistency in adjacent frames and background information penalization, we propose learning a response interference suppression (RIS) correlation filter to tackle this problem. Specifically, we introduce a RIS regularization into the DCF-based framework, which aims to keep the target area response consistent in adjacent frames and repress distractors’ response in the background. Besides, we adopt a response auxiliary strategy (RAS) to smooth the target response, which intends to obtain the precise location and avoid target drift. Furthermore, extensive experiments on three UAV benchmarks demonstrate the excellent performance of the proposed method against other 19 state-of-the-art trackers. Moreover, the tracking speed of the proposed method can reach 42 FPS on a single CPU.