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This paper describes a 3D vision system based on a new 3D sensor technology for the detection and classification of occupants in a car. New generation of so-called "smart airbags" require the information about the occupancy type and position of the occupant. This information allows a distinct control of the airbag inflation. In order to reduce the risk of injuries due to airbag deployment, the airbag can be suppressed completely in case of a child seat oriented in reward direction. In this paper, we propose a 3D vision system based on a 3D optical time-of-flight (TOF) sensor, for the detection and classification of the occupancy on the passenger seat. Geometrical shape features are extracted from the 3D image sequences. Polynomial classifier is considered for the classification task. A comparison of classifier performance with principle components (eigen-images) is presented. This paper also discusses the robustness of the features with variation of the data. The full scale tests have been conducted on a wide range of realistic situations (adults/children/child seats etc.) which may occur in a vehicle.