Unmanned aerial vehicle (UAV) target tracking tasks can currently be successfully completed in daytime situations with enough lighting, but they are unable to do so in nighttime scenes with inadequate lighting, poor contrast, and low signal-to-noise ratio. This letter presents an enhanced low-light enhancer for UAV nighttime tracking based on Zero-DCE++ due to its advantages of low processing cost and quick inference. We developed a light-weight UCBAM capable of integrating channel information and spatial features and offered a fully considered curve projection model in light of the low signalto-noise ratio of night scenes. This method significantly improved the tracking performance of the UAV tracker in night situations when tested on the public UAVDark135 and compared to other cutting-edge low-light enhancers. By applying our work to different trackers, this search shows how broadly applicable it is.

Visual tracking remains an open challenge, as it requires real-time and long-term accurate target prediction. Siamese network has been widely studied due to its excellent accuracy and speed. Since long-term tracking may lead to model degradation and drift, most existing algorithms cannot well solve this problem. This article proposes a new Siamese Network based on Fast Attention Network named SiamFA. This method designs an attention model, which can enhance the key and global information of the target, to obtain a more robust target model and achieve long-term tracking. At the same time, the attention model is used to obtain the potential position information of the target when calculating the similarity between the template and the search area. In addition, the attention network we design reduces many redundant operations and effectively improves computational efficiency. We utilize a multi-layer perceptron to forecast the bounding box to avoid excessive hyper-parameters. In order to verify the effectiveness of our network, we conduct tests on many commonly used datasets, such as OTB100, GOT-10k, LaSOT, TrackingNet, UAV123. Our method can achieve a success rate of 62.7% and the precision rate of 64.3% on LaSOT. At the same time, it can run at about 100fps, which exceeds the comparison network, proving that our network can run in real-time.