Real-time occupant detection system in an active illumination environment

A single grey-scale camera based object classification system for vehicle airbag deployment control in wide and frequent illumination variations is introduced. Image sequences are acquired using an active illumination systems that is used to minimise the effects of the widely varying levels of ambient illumination, combined with a means of shadow suppression. Twodimensional information of the object is extracted by employing the active contour model, based on a priori knowledge of the passenger behavior. A triplet of images, of which each image is illuminated from a different direction, are sequentially used by the photometric stereo method to recover the three-dimensional shape of the object. Utilizing both the two and threedimensional properties of the object, a 29-dimensional feature vector is defined for the training of a neural network designed to solve a three-class problem, with the classes being forward-facing child seat, rear-facing child seat, and adult. The system is tested on a database of over 84,000 frames collected from a wide range of objects in various illumination conditions. A classification accuracy of 98.9% was achieved within the decision-time limit of three seconds.