Real-time people tracking for mobile robots using thermal vision

This paper presents a vision-based approach for tracking people on a mobile robot using thermal images. The approach combines a particle filter with two alternative measurement models that are suitable for real-time tracking. With this approach a person can be detected independently from current light conditions and in situations where no skin colour is visible. In addition, the paper presents a comprehensive, quantitative evaluation of the different methods on a mobile robot in an office environment, for both single and multiple persons. The results show that the measurement model that was learned from local greyscale features could improve on the performance of the elliptic contour model, and that both models could be combined to further improve performance with minimal extra computational cost.