The thesis presents a novel approach for people detection and tracking in industrial environments shared between machines and humans. Addressing safety critical applications, we make the basic assumption that people wear reflective vests. In order to detect these vests and to discriminate them from other reflective materials, we propose an approach based on a single camera system equipped with an infrared flash and an infrared bandpass filter.
The camera acquires pairs of images, one with and one without IR flash, in short succession. The image pairs are related to each other through feature detection and tracking, which allows to identify a set of interest points for which the relative intensity difference is high and which are thus believed to originate from a reflective vest. The local neighborhood of these features is then further observed. Based on a local image descriptor, a Random Forest classifier is applied to discriminate between features caused by a reflective vest and features caused by other reflective materials. For features classified as a reflective vest, the distance between camera and vest is estimated by a Random Forest regressor, again on the basis of the local image descriptor. The distance estimates combined with the intrinsic camera model allow to estimate the 3D position relative to the camera for every vest feature. Finally, a particle filter incorporates the single position estimates and keeps track of the position of a reflective vest over time.
The proposed system is evaluated in several indoor and outdoor environments and under different weather conditions. The results indicate good classification performance and promising accuracy in position estimation and tracking.