A Novel Tuning Method for Real-time Multiple-Object Tracking Utilizing Thermal Sensor with Complexity Motion Pattern
Journal:
arXiv
Published Date:
Jul 3, 2025
Abstract
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.
