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) intrusion, anti-UAV techniques are becoming increasingly demanding. Object tracking, especially in thermal infrared (TIR) videos, though provides a promising solution, struggles with challenges like small scale and fast movement that commonly occur in anti-UAV scenarios. To mitigate this, we propose a simple yet effective spatio-temporal attention based Siamese network, dubbed SiamSTA, to track UAV robustly by performing reliable local tracking and wide-range re-detection alternatively. Concretely, tracking is carried out by posing spatial and temporal constraints on generating candidate proposals within local neighborhoods, hence eliminating background distractors to better perceive small targets. Complementarily, in case of target lost from local regions due to fast movement, a three-stage re-detection mechanism is introduced to re-detect targets from a global view by exploiting valuable motion cues through a correlation filter based on change detection. Finally, a state-aware switching policy is adopted to adaptively integrate local tracking and global re-detection and take their complementary strengths for robust tracking. Extensive experiments on the 1st and 2nd anti-UAV datasets well demonstrate the superiority of SiamSTA over other competing counterparts. Notably, SiamSTA is the foundation of the 1st-place winning entry in the 2nd Anti-UAV Challenge.

Discriminative Correlation Filter (DCF) based trackers are quite efficient in tracking objects by exploiting the circulant structure. The kernel trick further improves the performance of such trackers. The unwanted boundary effects, however, are difficult to solve in the kernelized correlation models. In this paper, we propose a novel Constrained Multi-Kernel Correlation tracking Filter (CMKCF), which applies spatial constraints to address this drawback. We build the multi-kernel models for multi-channel features with three different attributes, and then employ a spatial cropping operator on the semi-kernel matrix to address the boundary effects. For the constrained optimization solution, we develop an Alternating Direction Method of Multipliers (ADMM) based algorithm to learn our multi-kernel filters efficiently in the frequency domain. In particular, we suggest an adaptive updating mechanism by exploiting the feedback from high-confidence tracking results to avoid corruption in the model. Extensive experimental results demonstrate that the proposed method performs favorably on OTB-2013, OTB-2015, VOT-2016 and VOT-2018 dataset against several state-of-the-art methods.

Visual object tracking with semantic deep features has recently attracted much attention in computer vision. Especially, Siamese trackers, which aim to learn a decision making-based similarity evaluation, are widely utilized in the tracking community. However, the online updating of the Siamese fashion is still a tricky issue due to the limitation, which is a tradeoff between model adaption and degradation. To address such an issue, in this article, we propose a novel attentional transfer learning-based Siamese network (SiamATL), which fully exploits the previous knowledge to inspire the current tracker learning in the decision-making module. First, we explicitly model the template and surroundings by using an attentional online update strategy to avoid template pollution. Then, we introduce an instance-transfer discriminative correlation filter (ITDCF) to enhance the distinguishing ability of the tracker. Finally, we suggest a mutual compensation mechanism that integrates cross-correlation matching and ITDCF detection into the decision-making subnetwork to achieve online tracking. Comprehensive experiments demonstrate that our approach outperforms state-of-the-art tracking algorithms on multiple large-scale tracking datasets.

Automatic subcutaneous vessel imaging with near-infrared (NIR) optical apparatus can promote the accuracy of locating blood vessels, thus significantly contributing to clinical venipuncture research. Though deep learning models have achieved remarkable success in medical image segmentation, they still struggle in the subfield of subcutaneous vessel segmentation due to the scarcity and low-quality of annotated data. To relieve it, this work presents a novel semi-supervised learning framework, SCANet, that achieves accurate vessel segmentation through an alternate training strategy. The SCANet is composed of a multi-scale recurrent neural network that embeds coarse-to-fine features and two auxiliary branches, a consistency decoder and an adversarial learning branch, responsible for strengthening fine-grained details and eliminating differences between ground-truths and predictions, respectively. Equipped with a novel semi-supervised alternate training strategy, the three components work collaboratively, enabling SCANet to accurately segment vessel regions with only a handful of labeled data and abounding unlabeled data. Moreover, to mitigate the shortage of annotated data in this field, we provide a new subcutaneous vessel dataset, VESSEL-NIR. Extensive experiments on a wide variety of tasks, including the segmentation of subcutaneous vessels, retinal vessels, and skin lesions, well demonstrate the superiority and generality of our approach.

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.