融合热成像身份与运动相似性，提升复杂场景下目标追踪精度。
Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Motion Similarity
- 基于热特征与运动模式联合匹配，解决热成像特征稀疏问题。
- 在UrbanThermal-RGB数据集上，MOTA提升4.2%，对遮挡更鲁棒。
- 开源数据集与代码，适合热成像追踪与多模态算法研究者。
热成像下的多目标追踪（MOT）因缺乏视觉特征和运动模式复杂而面临独特挑战。本文提出一种新型框关联方法，融合热目标身份信息与运动相似性，有效结合热特征稀疏性与动态追踪需求，显著提升追踪准确率与鲁棒性。我们构建了一个大规模热成像与可见光图像数据集，覆盖多样城市环境，既作为本方法的基准，也为热成像研究提供新资源。大量实验表明，该方法在多种条件下均优于现有方法，尤其在遮挡与低对比度场景中表现优异。研究成果验证了热身份与运动信息融合的有效性。相关数据集与源码已公开于https://github.com/wassimea/thermalMOT。
原文摘要 · Abstract (English)
Multiple Object Tracking (MOT) in thermal imaging presents unique challenges due to the lack of visual features and the complexity of motion patterns. This paper introduces an innovative approach to improve MOT in the thermal domain by developing a novel box association method that utilizes both thermal object identity and motion similarity. Our method merges thermal feature sparsity and dynamic object tracking, enabling more accurate and robust MOT performance. Additionally, we present a new dataset comprised of a large-scale collection of thermal and RGB images captured in diverse urban environments, serving as both a benchmark for our method and a new resource for thermal imaging. We conduct extensive experiments to demonstrate the superiority of our approach over existing methods, showing significant improvements in tracking accuracy and robustness under various conditions. Our findings suggest that incorporating thermal identity with motion data enhances MOT performance. The newly collected dataset and source code is available at https://github.com/wassimea/thermalMOT
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