A Spatial-semantic Combine Perception for Infrared UAV Target Tracking

YU Guodong; JIANG Yichun; LIU Yunqing; WANG Yijun; ZHAN Weida; WANG Chunyang; FENG Jianghai; HAN Yueyi

doi:10.11999/JEIT250613

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YU Guodong, JIANG Yichun, LIU Yunqing, WANG Yijun, ZHAN Weida, WANG Chunyang, FENG Jianghai, HAN Yueyi. A Spatial-semantic Combine Perception for Infrared UAV Target Tracking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250613

Citation:

YU Guodong, JIANG Yichun, LIU Yunqing, WANG Yijun, ZHAN Weida, WANG Chunyang, FENG Jianghai, HAN Yueyi. A Spatial-semantic Combine Perception for Infrared UAV Target Tracking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250613

Citation:

YU Guodong, JIANG Yichun, LIU Yunqing, WANG Yijun, ZHAN Weida, WANG Chunyang, FENG Jianghai, HAN Yueyi. A Spatial-semantic Combine Perception for Infrared UAV Target Tracking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250613

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A Spatial-semantic Combine Perception for Infrared UAV Target Tracking

doi: 10.11999/JEIT250613 cstr: 32379.14.JEIT250613

1.
Unit 63869, People’s Liberation Army, Baicheng 137001, China
2.
School of Electronical and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China

Funds: Jilin Province Development and Reform Commission Special Fund for Innovation Capacity Development (2024C021-8)

Rev Recd Date: 2025-10-14

Available Online: 2025-10-16

Abstract

Abstract

Objective In recent years, infrared image-based UAV target tracking technology has attracted widespread attention. In real-world scenarios, infrared UAV target tracking still faces significant challenges due to factors such as complex backgrounds, UAV target deformation, and camera movement. Siamese network-based tracking methods have made breakthroughs in balancing tracking accuracy and efficiency. However, existing approaches rely solely on high-level feature outputs from deep networks to predict target positions, neglecting the effective use of low-level features. This leads to the loss of spatial detail features of infrared UAV targets, severely affecting tracking performance. To efficiently utilize low-level features, some methods have incorporated Feature Pyramid Networks (FPN) into the tracking framework, progressively fusing cross-layer feature maps in a top-down manner, thereby effectively enhancing tracking performance for multi-scale targets. Nevertheless, these methods directly adopt traditional FPN channel reduction operations, which result in significant loss of spatial contextual information and channel semantic information. To address the above issues, a novel infrared UAV target tracking method based on spatial-semantic combine perception is proposed. By capturing spatial multi-scale features and channel semantic information, the proposed approach enhances the model's capability to track infrared UAV targets in complex backgrounds. Methods The proposed method comprises four main components: a backbone network, multi-scale feature fusion, template-search feature interaction, and a detection head. Initially, template and search images containing infrared UAV targets are input into a weight-sharing backbone network to extract features. Subsequently, a FPN is constructed, within which a Spatial-semantic Combine Attention Module (SCAM) is integrated to efficiently fuse multi-scale features. Finally, a Dual-branch global Feature interaction Module (DFM) is employed to facilitate feature interaction between the template and search branches, and the final tracking results are obtained through the detection head. The proposed SCAM enhances the network’s focus on spatial and semantic information by jointly leveraging spatial and channel attention mechanisms, thereby mitigating the loss of spatial and semantic information in low-level features caused by channel dimensionality reduction in traditional FPN. SCAM primarily consists of two components: the Spatial Multi-scale Attention module (SMA) and the Global-Local Channel Semantic Attention module (GCSA). The SMA captures long-range multi-scale dependencies efficiently through axial positional embedding and multi-branch grouped feature extraction, thereby improving the network's perception of global contextual information. GCSA adopts a dual-branch design to effectively integrate global and local information across feature channels, suppress irrelevant background noise, and enable more rational channel-wise feature weighting. The proposed DFM treats the template branch features as the query source for the search branch and applies global cross-attention to capture more comprehensive features of infrared UAV targets. This enhances the tracking network’s ability to attend to the spatial location and boundary details of infrared UAV targets. Results and Discussions The proposed method has been validated on the infrared UAV benchmark dataset (Anti-UAV). Quantitative analysis (Table 1) demonstrates that, compared to 10 state-of-the-art methods, the proposed approach achieves the highest average normalized precision score of 76.9%, surpassing the second-best method, LGTrack, by 4.4%. In terms of success rate and localization precision (Fig. 6), the proposed method also outperforms LGTrack by 4.7% and 2.1%, respectively, evidencing its superiority in infrared UAV target tracking. Qualitative analysis (Figs. 7-11) further confirms that the proposed method exhibits strong adaptability and robustness when addressing various typical challenges in infrared UAV tracking, such as occlusion, distracting objects, complex backgrounds, scale variations, and rapid deformations. The collaborative design of the individual modules significantly enhances the model’s ability to perceive and represent small targets and dynamic scenes. In addition, qualitative experiments (Fig. 12) conducted on a self-constructed infrared UAV tracking dataset demonstrate the effectiveness and generalization capability of the proposed method in real-world tracking scenarios. Ablation studies (Tables 2-6) reveal that integrating any individual proposed module consistently improves tracking performance. Compared with the baseline tracker, the integration of all sub-modules leads to improvements of 14.3% in average normalized precision, 12.5% in success rate, and 14.0% in localization precision, thereby verifying the effectiveness of the proposed components. Conclusions This paper conducts a systematic theoretical analysis and experimental validation addressing the issue of spatial and semantic information loss in infrared UAV target tracking. Focusing on the limitations of existing FPN-based infrared UAV tracking methods, particularly the drawbacks associated with channel reduction in multi-scale low-level features, a novel infrared UAV target tracking method based on spatial-semantic combine perception is proposed which fully leverages the complementary advantages of spatial and channel attention mechanisms. This method enhances the network’s focus on spatial context and critical semantic information, thereby improving overall tracking performance. The following main conclusions are obtained: (1) Proposed SCAM combining SMA and GCSA, where SMA captures spatial long-range feature dependencies through position coordinate embedding and one-dimensional convolution operations, ensuring the acquisition of multi-scale contextual information, while GCSA achieves more comprehensive semantic feature attention by interacting local and global channel features. (2) Designed DFM, which realizes feature interaction between search branch features and template branch features through global cross-attention, enabling the dual-branch features to complement each other and enhancing network tracking performance. (3) Extensive experimental results demonstrate that the proposed algorithm outperforms existing advanced methods in both quantitative evaluation and qualitative analysis, with an average state accuracy of 0.769, success rate of 0.743, and precision of 0.935, achieving more accurate tracking of infrared UAV targets. Although the algorithm in this paper has been optimized in terms of computing resource utilization efficiency, further research is needed on efficient deployment strategies for embedded and mobile devices to improve real-time performance and computing adaptability.
- Target tracking,
- Semantic information,
- Spatial context information,
- Long-range dependence,
- Feature interaction

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References(26)

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