Integrated segmentation and classification for automotive airbag suppression

Abstract

The use of airbags into automobiles has significantly improved the safety of the occupants. Unfortunately, when airbags are deployed in the case of a crash, they can also cause fatal injuries if the occupant is a child smaller (in weight) than a typical 6 year old. In response to this, The National Highway Transportation and Safety Administration (NHTSA) has mandated that, starting in the 2006 model year, all automobiles be equipped with an automatic suppression system. These systems are supposed to suppress the airbag if a child or an infant is occupying the front passenger seat. We are investigating the use of machine vision to classify front-seat passenger occupants into four classes: (i) adult, (ii) empty, (iii) RFIS (rear facing infant seat), and child. The design and integration of such a vision system into automobiles is very difficult due to (i) occupant variability (e.g., different types of infant seats and children clothing), and (ii) extreme lighting variability (e.g., bright sunny days, and night time operation). Our approach is based on recognition-driven segmentation. Preliminary results show that by integrating the segmentation and classification stages of processing, we are able to more reliably recognize the various occupant classes.