Occupant classification system for automotive airbag suppression

Abstract

The introduction of airbags into automobiles has significantly improved the safety of the occupants. Unfortunately, airbags 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 to detect the presence of a child or infant and suppress the airbag. The classification problem we address is a four-class problem with the classes being rear-facing infant seat, child, adult, and empty seat. We describe a machine vision-based occupant classification system using a single grayscale camera and a digital signal processor that can perform this function in real time (< 5 seconds). The system has been extensively tested on a database of over 21,000 real-world images collected over a period of 4 months in moderate lighting conditions with a wide variety of passengers in eight different vehicles. We have achieved a classification accuracy of /spl sim/ 95%. We believe this system serves the need for a low-cost, high reliability embedded real-time airbag suppression system. Additional testing and improvements of the classification system are currently underway.