Tracking moving targets in satellite videos has attracted wide attention recently. However, the development of target tracking in satellite videos is much slower than that in general videos for the following key reasons. First, typical moving objects in satellite videos consist of few pixels and lose most of their appearance features, making it difficult for the tracker to distinguish the target from the background. Second, the appearance of satellite video objects often changes due to occlusion, illumination variation, or other factors. Classic Siamese tracking networks only use the first frame as the target template, leading to poor tracking results. Third, when the target is fully occluded, it is difficult for the tracker to recapture the target. To address the above problems, we propose a Siamese tracking network based on multiple(M) response map fusion and spatiotemporal constraints in this paper. By generating response maps at different layers of the tracking network and fusing them adaptively, small objects in the satellite videos can be tracked more accurately. Furthermore, a dynamic(D) template update strategy is proposed to cope with possible changes in the appearance of objects in satellite videos, preventing the high dependence on the initial frame. To recapture the target, a score-guided target motion(M) trajectory prediction model is proposed. We call the proposed Siamese tracking network SiamMDM for short. We conducted complete experiments on SatSOT and SV248S, two large satellite video target tracking datasets. The results show that our method achieves state-of-the-art tracking performance while running at over 110 FPS.

Single-frame infrared small target (SIRST) detection has been applied in many civilian and military applications and has greatly developed since deep learning methods grow in recent years. However, the contradiction between semantic information and spatial details limits detection performance especially for dim and small targets with complex interference. To overcome the restrictions, we propose an asymmetric patch attention fusion network (APAFNet) in this letter to merge high-level semantics and low-level spatial details, which consists of an asymmetric patch attention fusion module based on a patch channel attention branch and a dilation context block, guiding the network to collect local semantics and spatial details. The experimental results on NUAA-SIRST dataset and IRSTD-1k dataset show that the proposed APAFNet can achieve excellent performance under complex backgrounds.

With the development of unmanned aerial vehicle (UAV) technology, the threat of UAV intrusion is no longer negligible. Therefore, drone perception, especially anti-UAV tracking technology, has gathered considerable attention. However, both traditional Siamese and transformer-based trackers struggle in anti-UAV tasks due to the small target size, clutter backgrounds and model degradation. To alleviate these challenges, a novel contrastive-augmented memory network (CAMTracker) is proposed for anti-UAV tracking tasks in thermal infrared (TIR) videos. The proposed CAMTracker conducts tracking through a two-stage scheme, searching for possible candidates in the first stage and matching the candidates with the template for final prediction. In the first stage, an instance-guided region proposal network (IG-RPN) is employed to calculate the correlation features between the templates and the searching images and further generate candidate proposals. In the second stage, a contrastive-augmented matching module (CAM), along with a refined contrastive loss function, is designed to enhance the discrimination ability of the tracker under the instruction of contrastive learning strategy. Moreover, to avoid model degradation, an adaptive dynamic memory module (ADM) is proposed to maintain a dynamic template to cope with the feature variation of the target in long sequences. Comprehensive experiments have been conducted on the Anti-UAV410 dataset, where the proposed CAMTracker achieves the best performance compared to advanced tracking algorithms, with significant advantages on all the evaluation metrics, including at least 2.40%, 4.12%, 5.43% and 5.48% on precision, success rate, success AUC and state accuracy, respectively.

Deep learning (DL)-based methods have shown great potential in the satellite video target tracking community. Nevertheless, most of the methods are still plagued by poor localization capabilities and background interference problems. In this paper, we propose EHTracker, a Siamese-based tracker based on enhanced head network, specifically for tiny targets in satellite videos. EHTracker enhances the localization capability for tiny objects through the proposed enhanced head network, while the proposed background suppression module (BSM) improves the network's ability to suppress background interference. Specifically, to alleviate the problem of overlapping between the foreground and background in the classification branch, we propose a multi-head graph attention refinement module (MGARM). Second, to enable the regression branch to determine the location of feature-poor small targets more accurately, we introduce a gradual regression strategy (GRS). MGARM and GRS together constitute the enhanced head network component of EHTracker. Further, in order to work with the enhanced head network to achieve classification and localization of small targets that lack distinctive features, we design a background suppression module. Extensive experiments are implemented on the SatSOT dataset and SV248S dataset, and the experimental results show that our method achieves state-of-the-art tracking performance.

Infrared (IR) small target detection is a crucial component of infrared imaging systems and is vital for applications in surveillance, security, and early warning systems. However, most existing algorithms for detecting small targets in infrared imagery encounter difficulties in achieving both high accuracy and speed, particularly in complex scenes. Additionally, infrared image sequences frequently exhibit gradual background changes as well as sudden alterations, which further complicates the task of detecting small targets. To address these issues, a dual-region search method (DRSM) is proposed and combined with multi-directional filtering, min-sum fusion, and clustering techniques, forming an infrared small moving target detection method in complex scenes. First, a multi-directional filter bank is proposed and it causes the original infrared image sequence to retain only point-like features after the filtering. Then, several consecutive filtered feature maps are superimposed into one, where the moving target will leave a trajectory due to its motion characteristics. Finally, based on the trajectory, a dual-region search strategy is employed to pinpoint the exact location of the target. The experimental outcomes show that, compared to alternative algorithms, the proposed approach outperforms others in terms of detection accuracy and speed, particularly in diverse real-world complex scenarios.