This thesis deals with the problem of classifying automotive vehicle occupants and estimating their position. This information is critical in designing future smart airbag systems providing maximum protection for passengers. According to NHTSA, since 1990, in the USA, 227 deaths have been attributed to airbags deployed in low-speed crashes which included 119 children, and 22 infants. In these cases, intelligent deployment of the airbag, based on the type and position of occupant could have avoided these fatalities. Current commercial classification systems based on traditional sensors, such as pressure sensors are not able to detect the position of occupants. Vision-based systems are dvantageous as they can provide additional functionalities like dynamic occupant position analysis or child seat orientation. On the other hand, vision-based systems have to cope with several challenges, such as, illumination conditions, temperature, humidity, large variation of scenes, cost, and computational aspects. This thesis presents new pattern recognition techniques for classifying, localizing and tracking vehicle occupants using a low-resolution 3D optical time-of-light range camera.

Данное издание не является оригинальным. Книга печатается по технологии принт-он-деманд после получения заказа.