Disparity Based Image Segmentation For Occupant Classification

Abstract

Frontal "depowered" air bag systems are underdesign today to be even more effective than current air bags in saving lives, while at the same time reducing the potential of causing an air bag induced serious injury or death. Stereovision real-time occupant sensing systems (airbag suppression) have been developed at Delphi Automotive Systems for use in comercial vehicle applications. One of the issues in such a system is that the irrelevant non-stationary background information within the field of view of the cameras results in less robust occupant classifications. In this paper, a disparity-based image segmentation method is provided. The input images are first segmented according to a pre-determined disparity threshold map and the real-time disparities of the occupants. Binary image processing techniques are used to reject noise introduced into the segmented images through low- resolution disparity calculations. The segmented images are then used for image feature extraction for a neural network classifier. For comparison, two neural network classifiers were created with and without the infrared image segmentation. Our experiements on segemnted images shown an increase of the classifier performance by at least 23% on a large database of IR images collected in extreme outdoor conditions.