针对热成像车辆重识别难题，提出视角感知特征选择方法
VC-FeS: Viewpoint-Conditioned Feature Selection for Vehicle Re-identification in Thermal Vision
- 根据视角动态选择特征，分离处理不同区域信息
- 在热成像数据集上提升mAP达19.7%和12.8%
- 适合做热成像下少关节物体识别的研究者参考
利用单通道热成像图像识别低关节性物体（如车辆）在监控等场景中至关重要。然而，由于缺乏颜色信息导致同类物体间相似度高（忽略形状），且纹理信息弱化，现有方法表现不佳；此外，视角变化使特征分布不一致，加剧识别难度。为此，本文构建视角条件化的特征向量，并在独立特征空间中进行区域特异性对比。该方法可有效利用已有的RGB预训练ViT特征提取器，同时适应热成像域的特殊挑战。我们在RGBNT100(IR)车辆数据集及自建的热成像海事数据集上进行测试，mAP分别超过现有最优方法19.7%和12.8%。我们还计划公开首个用于海事船只识别的热成像数据集。
原文摘要 · Abstract (English)
Identification of less-articulated objects using single-channel images, such as thermal images, is important in many applications, such as surveillance. However, in this domain, existing methods show poor performance due to high similarity among objects of the same category in the absence of color information (overlooking shape information) and de-emphasized texture information. Furthermore, variability in viewpoint adds more complexity as the features vary from side to side. We address these issues by constructing viewpoint-conditioned feature vectors and area-specific feature comparisons in separate feature spaces. These interventions enable leveraging the advancements of existing RGB-pre-trained ViT feature extractors while effectively adapting them to address the challenges specific to the thermal domain. We test our system with RGBNT100 (IR) vehicle dataset and a thermal maritime dataset acquired by us. Our results surpass the state-of-the-art methods by 19.7% and 12.8% for the above datasets in mAP scores, respectively. We also plan to make our thermal dataset available, the first of its kind for maritime vessel identification.
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