Seat occupation detection inside vehicles

In our paper we address the problem of robust seat occupation detection inside vehicles. The approach used consists of four steps: correction of distortions followed by an epipolar rectification of the stereo images, feature extraction, feature-based matching, and the seat occupation detection and verification. The focus in this paper is on the verification of the seat occupation. The step of verification corresponds to a classification of the driver and the passenger seat as occupied or empty. First, we try to estimate the seat geometry and localization. Implicitly it can be deduced from the results, that if a seat can be modeled adapted to the data, the seat is empty. Otherwise we can assume that the seat is occupied by an object. Then, we try to differ between an occupation by a human, or any other object. On tests on numerous image sequences recorded inside different vehicles the feasibility of the approach is shown.