Thermal Pedestrian Multiple Object Tracking Challenge (TP-MOT)

Abstract

Multiple Object Tracking (MOT) has seen significant advancements in the RGB domain, yet remains underexplored in thermal imaging, despite its advantages in low-light and adverse weather conditions. The Thermal Pedestrian Multiple Object Tracking (TP-MOT) Challenge addresses this gap by introducing a large-scale thermal dataset and a standardized evaluation framework. This challenge provides a benchmark for tracking algorithms designed specifically for thermal data, emphasizing robust detection, motion modeling, and identity association in infrared imagery. Participants were required to use a tracking-by-detection pipeline with standardized YOLO-based detectors, ensuring a fair comparison of tracking methodologies. The top-performing approaches leveraged adaptive hyperparameter tuning, motion-based association, and infrared-specific feature extraction to enhance tracking accuracy while maintaining computational efficiency. The results demonstrate that thermal MOT can achieve high performance with dedicated methodologies, offering new insights into tracking pedestrians in challenging conditions. In this first edition a total of 11 teams have been registered for participation. This challenge serves as a catalyst for future research, paving the way for improved thermal tracking solutions in surveillance, autonomous navigation, and security applications.