A Novel Tuning Method for Real-time Multiple-Object Tracking Utilizing Thermal Sensor with Complexity Motion Pattern

TL;DR

This paper presents a new hyperparameter tuning method for real-time multi-object tracking in thermal images, improving accuracy in challenging surveillance environments without complex reidentification models.

Contribution

It introduces a two-stage hyperparameter tuning framework specifically designed for thermal imagery, enhancing tracking performance in real-time scenarios.

Findings

Achieves high accuracy in thermal multi-object tracking

Effective across various thermal camera conditions

Does not rely on complex reidentification or motion models

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.