High performance sensor fusion architecture for vision-based occupant detection

Abstract

We describe a fast and reliable vision system for detecting and recognizing occupants in automobiles. The main advantage of our system is its high accuracy due to the use of fusion module, which combines the results of multiple classifiers operating on different types of features (edges, scale, and range) from the same image. Another advantage is that since the same image sensor is used to generate the multiple feature types, cost is reduced. We utilize an active illumination strategy to provide adequate illumination car seat and shadow fill-in during both the night and day. Occupant position detection and recognition is performed on the actively illuminated image using the same algorithm under both night and day conditions. Our system can use images from commercially available CMOS vision sensors and thus very cost-effective and efficient for smart air bag applications in automobiles. We have successfully demonstrated this system operating in a test vehicle at real-time video rates (30 updates/sec) with high accuracies for a large variety of situations and lighting conditions.