Real-time estimation of human body posture from monocular thermal images

TLDR

The study proposes a real‑time method for estimating human posture from monocular thermal images that works under varying background and lighting conditions. The approach uses distance transformation and center‑of‑gravity calculations, derives body orientation via moment of inertia, heuristically locates key points, and refines elbow and foot positions with a genetic‑algorithm learning procedure. Experiments demonstrate the algorithm’s robustness and real‑time performance at over 20 frames per second.

Abstract

This paper introduces a new real-time method to estimate the posture of a human from thermal images acquired by an infrared camera regardless of the back-ground and lighting conditions. Distance transformation is performed for the human body area extracted from the thresholded thermal image for the. Calculation of the center of gravity. After the orientation of the upper half of the body is obtained by calculating the moment of inertia, significant points such as the top of the head, the tips of the hands and foot are heuristically located. In addition, the elbow and foot positions are estimated from the detected (significant) points using a genetic algorithm based learning procedure. The experimental results demonstrate the robustness of the proposed algorithm and real-time (faster than 20 frames per second) performance.