A monocular vision-based occupant classification approach for smart airbag deployment

Occupant classification is essential to a smart airbag system that can either turn off or deploy in a less harmful way according to the type of the occupants in the front seat. This paper presents a monocular vision-based occupant classification approach to classify the occupants into five categories including empty seats, adults in normal position, adults out of position, front-facing child/infant seats, and rear-facing infant seats. The proposed approach consists of image representation and pattern classification. The image representation step computes Haar wavelets and edge features from the monochrome video frames. A support vector machine (SVM) classifier next determines the occupant category based on the representative features. We have tested our approach on a large variety of indoor and outdoor images acquired under various illumination conditions for occupants with different appearances, sizes and shapes. With a strict occupant exclusive training/testing split, our approach has achieved an average correct classification rate of 97.18% among the five occupant categories.