In our paper we address to the problem of robust seat occupation detection inside vehicles. The used approach 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.
The ability to construct CAD or other object models from edge and range data has a fundamental meaning in building a recognition and positioning system. While the problem of model fitting has been successfully addressed, the problem of efficient high accuracy and stability of the fitting is still an open problem. In the past researchers have used approximate distance functions rather than the real Euclidean distance because of computational efficiency. We now feel that machine speeds are sufficient to ask whether it is worth considering Euclidean fitting again. This paper address the problem of estimation of elliptical cylinder and cone surfaces to 3D data by a constrained Euclidean fitting. We study and compare the performance of various distance functions in terms of correctness, robustness and pose invariance, and present our results improving known fitting methods by closed form expressions of the real Euclidean distance.
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