针对热成像中行人跟踪难题，提出实时调优方法提升复杂运动场景下的追踪精度。
A Novel Tuning Method for Real-time Multiple-Object Tracking Utilizing Thermal Sensor with Complexity Motion Pattern
- 分两阶段优化超参数，适配热成像复杂运动模式
- 在PBVS Thermal MOT数据集上实现高精度实时追踪
- 无需复杂重识别或运动模型，适合实际安防部署
热成像中的多目标追踪对监控系统至关重要，尤其在可见光相机因低能见度或照明差而失效的场景下。热传感器通过捕捉红外信号增强识别能力，但其低层特征表示导致行人检测与追踪困难。为此，本文提出一种新型调优方法，专为应对热成像中复杂运动模式设计。所提框架采用两阶段优化，确保每个阶段使用最合适的超参数以最大化追踪性能。通过实时追踪的超参数微调，该方法在不依赖复杂重识别或运动模型的前提下实现高精度。在PBVS Thermal MOT数据集上的大量实验表明，该方法在多种热成像条件下均表现优异，具备良好的鲁棒性，适用于真实世界监控应用。
原文摘要 · Abstract (English)
Multi-Object Tracking in thermal images is essential for surveillance systems, particularly in challenging environments where RGB cameras struggle due to low visibility or poor lighting conditions. Thermal sensors enhance recognition tasks by capturing infrared signatures, but a major challenge is their low-level feature representation, which makes it difficult to accurately detect and track pedestrians. To address this, the paper introduces a novel tuning method for pedestrian tracking, specifically designed to handle the complex motion patterns in thermal imagery. The proposed framework optimizes two-stages, ensuring that each stage is tuned with the most suitable hyperparameters to maximize tracking performance. By fine-tuning hyperparameters for real-time tracking, the method achieves high accuracy without relying on complex reidentification or motion models. Extensive experiments on PBVS Thermal MOT dataset demonstrate that the approach is highly effective across various thermal camera conditions, making it a robust solution for real-world surveillance applications.
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