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收稿日期：2024-10-05，
修回日期：2025-01-04，
录用日期：2025-02-25，
网络出版日期：2025-02-26，
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目的
2
可见光-红外行人重识别（VI-ReID）因可见光与红外图像间的模态差异而面临挑战，现有方法在特征分辨力方面存在不足。本研究旨在设计一种全新算法以获取高分辨力的行人特征，弥补跨模态识别任务中的不足。
方法
2
本研究提出一种融合结构与视觉特征的VI-ReID算法，通过双流分支进行处理。首先，借助姿态估计提取骨骼关键点来生成结构特征图，通过图卷积网络（GCN）学习骨骼的结构化信息，以构建结构特征提取分支；同时，以ResNet50作为视觉提取分支获取图像视觉特征。在此基础上，提出结构-视觉跨模态注意力机制（SVIAM），融合骨骼和视觉特征，得到高分辨力的联合特征表示。此外，为增强骨骼特征的一致性，提出结构内聚损失（SCLoss）函数，持续优化骨骼特征，有效减少模态内差异，保障算法的稳定性与准确性。
结果
2
实验结果表明，所提出算法在SYSU-MM01数据集上表现卓越，相较于基线DEEN，在all search模式下，Rank-1准确率提高4.21%，mAP提高3.52%；在indoor search模式下，Rank-1准确率提高7.39%，mAP提高2.56%。
结论
2
本研究提出融合结构与视觉特征的VI-ReID算法，有效提升跨模态行人重识别的识别精度，并在复杂场景中展现较高的鲁棒性和准确性。
Objective
2
Visible-infrared person re-identification （VI-ReID） has emerged as a challenging task primarily due to the pronounced modal discrepancies between visible and infrared images. In the visible light spectrum， images are replete with vivid colors and intricate textures， yet they are highly susceptible to perturbations caused by varying illumination conditions. For instance， during dawn or dusk， the subdued light can distort the visual appearance of pedestrians， making it arduous to accurately discern their unique features. In contrast， infrared images， which predominantly capture thermal radiation， offer a distinct advantage in low-light or obscured scenarios. However， they lack the detailed visual cues present in their visible counterparts， such as clothing patterns or facial features. These fundamental differences have led to significant difficulties in achieving reliable person re-identification across modalities. Compounding this issue， existing methods have been found wanting in terms of feature discrimination. In numerous real-world datasets and scenarios， they struggle to distinguish between pedestrians with similar postures or occluded body parts， thereby compromising the overall accuracy and reliability of the recognition process.
Method
2
To address these challenges head-on， this research embarked on a journey to design an innovative algorithm capable of extracting high-resolution pedestrian features， with the ultimate aim of bridging the existing gaps in cross-modal recognition tasks.The methodological framework of this study centered around a novel VI-ReID algorithm that incorporated both structural and visual features， operating through a meticulously designed dual-stream branch architecture. The first step in this process involved the extraction of skeletal key points via advanced pose estimation techniques. By leveraging state-of-the-art algorithms， such as OpenPose or， in some cases， custom-developed variants with enhanced capabilities， we were able to precisely localize the key joints of the human body even in the presence of partial occlusions or extreme postural variations. This was achieved through a series of complex computational steps， beginning with the initial detection of body regions， followed by the refinement of joint positions based on anatomical constraints and probabilistic models. The extracted skeletal key points were then used to generate detailed structural feature maps， which served as the foundation for further analysis.Subsequently， a graph convolutional network （GCN） was employed to delve deep into the structured information encapsulated within the skeletal framework. The GCN architecture was meticulously designed， comprising multiple layers， each with a carefully calibrated node connection pattern. The choice of activation functions， was optimized to enhance the propagation of relevant information while suppressing noise. Additionally， to account for the varying importance of different joints in characterizing a person's gait or posture， weighted processing was implemented， ensuring that the most discriminative features were emphasized. This comprehensive approach enabled the construction of a highly effective structural feature extraction branch.Simultaneously， ResNet50， a renowned deep learning model renowned for its prowess in visual feature extraction， was adapted to serve as the visual extraction branch. In this context， a series of fine-tuning procedures were carried out to tailor the model to the unique characteristics of both visible and infrared images. This involved adjusting the pre-trained model's parameters based on the statistical properties of the target datasets， as well as devising innovative strategies for leveraging the outputs from different hierarchical levels of the network. For instance， by selectively combining features from the early and late layers， we were able to capture both low-level details and high-level semantic information. In some cases， attention mechanisms were integrated to further focus on the most salient visual regions， enhancing the overall discriminative power of the visual features.Building upon these two parallel streams， a Structure-Visual Inter-modal Attention Mechanism （SVIAM） was proposed to seamlessly fuse the skeletal and visual features. This mechanism was underpinned by a sophisticated computational process that involved the precise calculation of correlation weights between the two modalities. Through the use of detailed schematic diagrams and mathematical formulas， we were able to illustrate how the attention was distributed， highlighting the most relevant regions and features for recognition. Compared to simplistic concatenation or rudimentary fusion methods， SVIAM demonstrated a remarkable superiority in terms of feature integration， leading to a more cohesive and discriminative joint feature representation.Furthermore， to bolster the consistency of the skeletal features and mitigate intra-modal differences， a Structure Cohesion Loss （SCLoss） function was devised. The mathematical formulation of SCLoss was derived with great care， taking into account the geometric and topological properties of the skeletal data. Each parameter within the function was meticulously calibrated to serve a specific purpose， whether it was to penalize deviations from the expected skeletal structure or to encourage the alignment of related joints. Through extensive experimental validation and theoretical analysis， we demonstrated how SCLoss effectively optimized the skeletal features， thereby enhancing the overall stability and accuracy of the algorithm.
Result
2
The experimental results provided unequivocal evidence of the algorithm's superiority. On the widely recognized SYSU-MM01 dataset， our proposed algorithm outperformed the baseline DEEN by significant margins. In the all search mode， the Rank-1 accuracy rate witnessed a remarkable boost of 4.21%， while the mean Average Precision （mAP） soared by 3.52%. Similarly， in the indoor search mode， the Rank-1 accuracy rate achieved an even more impressive increase of 7.39%， accompanied by a 2.56% elevation in mAP. These results not only validated the effectiveness of our approach in enhancing cross-modal person re-identification accuracy but also showcased its robustness and reliability in complex scenarios.
Conclusion
2
this research introduced a pioneering VI-ReID algorithm that integrated structural and visual features， effectively surmounting the challenges posed by modal differences and substantially elevating the recognition precision in cross-modal person re-identification. The algorithm's performance in complex and dynamic environments further attested to its high level of robustness and accuracy， paving the way for future advancements in this critical area of research.
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