In [3], a low-resolution range sensor was investigated for an occupant classification system that distinguish person from child seats or an empty seat. The optimal deployment of vehicle airbags for maximum protection moreover requires information about the occupant's size and position. The detection of occupant's position involves the detection and localization of occupant's head. This is a challenging problem as the approaches based on local shape analysis (in 2D or 3D) alone are not robust enough as other parts of the person's body like shoulders, knee may have similar shapes as the head. This paper discusses and investigate the potential of a Reeb graph approach to describe the topology of vehicle occupants in terms of a skeleton. The essence of the proposed approach is that an occupant sitting in a vehicle has a typical topology which leads to different branches of a Reeb Graph and the possible location of the occupant's head are thus the end points of the Reeb graph. The proposed method is applied on real 3D range images and is compared to Ground truth information. Results show the feasibility of using topological information to identify the position of occupant's head.

The deployment of vehicle airbags for maximum protection requires information about the occupant's position, movement, weight, size etc. Specifically, it is desirable to discriminate between adults, children, front or rear-facing child seats, objects put on the seat or simply empty seats. 2D images lack depth information about the object and are very sensitive to illumination conditions. Occupant position classification techniques are developed based on low resolution 3D image sequences. The proposed methods are of low complexity and high reliability, allowing real time implementation and meeting the rigorous requirements for passenger safety systems. Features are extracted from the 3D image sequences and a sequential forward search (SFS) feature subset selection algorithm is employed to reduce the size of the feature set. Two classification techniques are evaluated, the Bayes quadratic classifier and the polynomial classifier. We present the classification results based on a large set of measurements from low resolution 3D image sequences. Full scale tests have been conducted on a wide range of realistic situations (adults/children/child seats etc.) which may occur in a vehicle.