Fusion of Range and Thermal Images for Person Detection

TL;DR

This paper presents a system that fuses range and thermal infrared images to improve person detection accuracy, addressing challenges caused by appearance variations in traditional imaging methods.

Contribution

It introduces a novel fusion approach combining range and thermal data, along with calibration and data fusion algorithms, for enhanced person detection and tracking.

Findings

Fused data improves detection robustness against pose and clothing variations

Calibration method effectively aligns range and thermal images

Initial experiments show promising detection and tracking results

Abstract

Detecting people in images is a challenging problem. Differences in pose, clothing and lighting, along with other factors, cause a lot of variation in their appearance. To overcome these issues, we propose a system based on fused range and thermal infrared images. These measurements show considerably less variation and provide more meaningful information. We provide a brief introduction to the sensor technology used and propose a calibration method. Several data fusion algorithms are compared and their performance is assessed on a simulated data set. The results of initial experiments on real data are analyzed and the measurement errors and the challenges they present are discussed. The resulting fused data are used to efficiently detect people in a fixed camera set-up. The system is extended to include person tracking.