Huamin Tao

Infrared small-target tracking is a fundamental yet challenging task in computer vision, owing to extremely small target sizes, severe background clutter, and low signal-to-noise ratios, which often cause tracking drift or complete target loss. As a representative method in this field, SiamDT has achieved promising performance on the challenging Anti-UAV410 benchmark—a large-scale thermal infrared dataset. However, SiamDT still suffers from three key limitations: first, its standard Feature Pyramid Network (FPN) lacks channel-wise selective focusing, causing target-related channels to be overwhelmed by numerous background clutter channels; second, its fixed receptive field cannot adapt to the drastic scale variations in infrared targets; third, its spatial-domain feature fusion lacks high-frequency component compensation, resulting in severe loss of discriminative edge and contour details in deep features. To address these issues, we propose Enhanced-SiamDT, an enhanced tracking model built upon the SiamDT framework. Our contributions are twofold. First, we design a Multi-Attention Feature Pyramid Network (MA-FPN) that sequentially integrates Efficient Channel Attention (ECA), Large Selective Kernel (LSK), and Wavelet Domain Attention (WDAM) to achieve channel-wise recalibration, adaptive multi-scale receptive field selection, and frequency-domain detail enhancement, thereby suppressing background clutter and strengthening small-target feature representations. Second, building upon the dual similarity learning architecture inherited from the SiamDT baseline, we introduce a background prototype suppression strategy that reduces false alarms by penalizing candidate boxes with high similarity to background prototypes and a conservative template update mechanism with explicit update criteria, which prevents template drift under fast motion and short-term occlusion. Extensive experiments on the Anti-UAV410 and Anti-UAV benchmarks demonstrate that Enhanced-SiamDT achieves new state-of-the-art performance, with a State Accuracy (SA) of 68.58% and 71.84%, and a Precision of 89.92% and 92.71%, respectively. These results validate that our approach effectively overcomes the limitations of existing Siamese trackers, delivering significant improvements in discriminative feature extraction and tracking robustness for infrared small targets.

Unmanned Aerial Vehicle (UAV) vehicle-tracking technology has extensive potential for application in various fields. In the actual tracking process, the relative movement of the UAV and vehicles will bring large target-scale variations (i.e., size and aspect ratio change), which leads to missed detection and ID switching. Traditional tracking methods usually use multi-scale estimation to adaptively update the target scale for variable-scale detection and tracking. However, this approach requires selecting multiple scaling factors and generating a large number of bounding boxes, which results in high computational costs and affects real-time performance. To tackle the above issue, we propose a novel multi-target tracking method based on the BoT-SORT framework. Firstly, we propose an FB-YOLOv8 framework to solve the missed detection problem. This framework incorporates a Feature Alignment Aggregation Module (FAAM) and a Bidirectional Path Aggregation Network (BPAN) to enhance the multi-scale feature fusion. Secondly, we propose a multi-scale feature-fusion network (MSFF-OSNet) to extract appearance features, which solves the ID switching problem. This framework integrates the Feature Pyramid Network (FPN) and Convolutional Block Attention Module (CBAM) into OSNet to capture multilevel pixel dependencies and combine low-level and high-level features. By effectively integrating the FB-YOLOv8 and MSFF-OSNet modules into the tracking pipeline, the accuracy and stability of tracking are improved. Experiments on the UAVDT dataset achieved 46.1% MOTA and 65.3% IDF1, which outperforms current state-of-the-art trackers. Furthermore, experiments conducted on sequences with scale variations have substantiated the improved tracking stability of our proposed method under scale-changing conditions.