The perception of drones, also known as Unmanned Aerial Vehicles (UAVs), particularly in infrared videos, is crucial for effective anti-UAV tasks. However, existing datasets for UAV tracking have limitations in terms of target size and attribute distribution characteristics, which do not fully represent complex realistic scenes. To address this issue, we introduce a generalized infrared UAV tracking benchmark called Anti-UAV410 . The benchmark comprises a total of 410 videos with over 438 K manually annotated bounding boxes. To tackle the challenges of UAV tracking in complex environments, we propose a novel method called Siamese drone tracker ( SiamDT ). SiamDT incorporates a dual-semantic feature extraction mechanism that explicitly models targets in dynamic background clutter, enabling effective tracking of small UAVs. The SiamDT method consists of three key steps: Dual-Semantic RPN Proposals (DS-RPN) , Versatile R-CNN (VR-CNN) , and Background Distractors Suppression . These steps are responsible for generating candidate proposals, refining prediction scores based on dual-semantic features, and enhancing the discriminative capacity of the trackers against dynamic background clutter, respectively. Extensive experiments conducted on the Anti-UAV410 dataset and three other large-scale benchmarks demonstrate the superior performance of the proposed SiamDT method compared to recent state-of-the-art trackers.

With the growing threat of unmanned aerial vehicle (UAV) intrusions, the topic of anti-UAV tracking has received widespread attention from the community. Traditional Siamese trackers struggle with small UAV targets and are plagued by model degradation issues. To mitigate this, we propose a novel searching region-free and template-free Siamese network (SiamSRT) to track UAV targets in thermal infrared (TIR) videos. The proposed tracker builds a two-stage Siamese architecture with the former providing detection of the first-frame ground truth by using a cross-correlated region proposal network (C-C RPN) and the latter providing detection of previous-frame predictions via a similarity-learning region convolutional neural network (S-L RCNN). In both stage, global proposals are acquired by region of interest (ROI) alignment operation to break the limitation of searching region. Then, a spatial location consistency function is introduced to suppress background thermal distractors and a temporal memory bank (TMB) is utilized to avoid template update degradation problem. Further, a single-category foreground detector (SCFD) is designed to independently predict the position of the UAV target. SCFD can re-initialize the tracker without the given target in the first frame, which can help to recover the tracking failures. Comprehensive experiments demonstrate that SiamSRT achieves the best performance compared to the most advanced algorithms in the anti-UAV tracking missions.

The popularity of unmanned aerial vehicles (UAVs) has made anti-UAV technology increasingly urgent. Object tracking, especially in thermal infrared videos, offers a promising solution to counter UAV intrusion. However, troublesome issues such as fast motion and tiny size make tracking infrared drone targets difficult and challenging. This work proposes a simple and effective spatio-temporal attention based Siamese method called SiamSTA, which performs reliable local searching and wide-range re-detection alternatively for robustly tracking drones in the wild. Concretely, SiamSTA builds a two-stage re-detection network to predict the target state using the template of first frame and the prediction results of previous frames. To tackle the challenge of small-scale UAV targets for long-range acquisition, SiamSTA imposes spatial and temporal constraints on generating candidate proposals within local neighborhoods to eliminate interference from background distractors. Complementarily, in case of target lost from local regions due to fast movement, a third stage re-detection module is introduced, which exploits valuable motion cues through a correlation filter based on change detection to re-capture targets from a global view. Finally, a state-aware switching mechanism is adopted to adaptively integrate local searching and global re-detection and take their complementary strengths for robust tracking. Extensive experiments on three anti-UAV datasets nicely demonstrate SiamSTA’s advantage over other competitors. Notably, SiamSTA is the foundation of the 1st-place winning entry in the 2nd Anti-UAV Challenge.

Discriminative correlation filter (DCF)-based methods have demonstrated superior performance in UAV tracking via fusing multiple types of features and updating models online. However, most DCF-based trackers simply cascade different features, failing to fully take advantage of their complementary strength. In addition, online update strategies are limited to using a single and fixed learning rate, which often leads to model degradation when suffering tracking challenges. In this paper, we present an Auto-Perceiving Correlation Filter (APCF) which explicitly models the target and context with a novel Target State and Background Perception (TSBP) feature. Concretely, we first propose a simple yet effective State Evaluation Metric (SEM) to estimate target states by analyzing the spatial distribution of responses. Based on SEM, we extract TSBP features by adaptively selecting effective features depending on the current target state. Accordingly, a new online model update strategy is also introduced to avoid model degradation. Moreover, we further introduce a perception regularization term to make the extracted feature emphasis more on the target rather than background. Extensive experiments on four widely-used UAV benchmarks have well demonstrated the superiority of the proposed method compared with both DCF and deep learning based trackers while running at a high speed of 76.7 FPS on a single CPU. In addition, APCF with deep features also performs favorably against state-of-the-art trackers.

Unmanned aerial vehicle (UAV) tracking focus on tracking moving targets from flying platforms, where the target undergoes a lot of aspect ratio changes, that is, viewpoint change and rotation. The discriminative correlation filter (DCF)-based method shows a promising solution to UAV tracking due to its high computational efficiency. DCF-based trackers apply a fixed-bandwidth Gaussian function label for model training and incremental update to adapt to the dramatic appearance changes in the target during tracking. However, due to the poor regression ability of the correlation filter, DCF trackers are unable to describe the target state when the aspect ratio changes, thus hurting the tracking performance. To alleviate this, we propose a novelty correlation filter, which constructs a Gaussian-like (GL) function label for correlation filter training. The label fully considers the aspect ratio distribution of the target, which facilitates the training of a more robust tracker. Furthermore, an accurate incremental update is proposed to mitigate model degradation by combining target samples with adaptive aspect ratios. Extensive experiments are conducted on three popular UAV benchmarks, that is, VisDrone2018-test-dev, UAV20L, and DTB70. Results well demonstrate the superiority of the proposed method over both DCF and deep-based trackers.

The tracking performance of discriminative correlation filters (DCFs) is often subject to unwanted boundary effects. Many attempts have already been made to address the above issue by enlarging searching regions over the last years. However, introducing excessive background information makes the discriminative filter prone to learn from the surrounding context rather than the target. In this article, we propose a novel context restrained correlation tracking filter (CRCTF) that can effectively suppress background interference via incorporating high-quality adversarial generative negative instances. Concretely, we first construct an adversarial context generation network to simulate the central target area with surrounding background information at the initial frame. Then, we suggest a coarse background estimation network to accelerate the background generation in subsequent frames. By introducing a suppression convolution term, we utilize generative background patches to reformulate the original ridge regression objective through circulant property of correlation and a cropping operator. Finally, our tracking filter is efficiently solved by the alternating direction method of multipliers (ADMM). CRCTF demonstrates the accuracy performance on par with several well-established and highly optimized baselines on multiple challenging tracking datasets, verifying the effectiveness of our proposed approach.