Fusion of Motion Information with Static Classifications of Occupant Images for Smart Airbag Applications

In many real-time object recognition applications, the system experiences conditions where the classification results are not reliable due to a variety of environmental or object poses where correct classification is difficult or even impossible. We propose that by tracking the motion and orientation of the object of interest, and fusing this information with the classification results, we can greatly improve the classifier performance. This can be achieved firstly by estimating the reliability of the classification, and secondly by using track state estimates to derive additional classification cues. We develop a framework based on interacting multiple model (IMM) Kalman filtering, Dempster-Shafer evidential reasoning and fuzzy set memberships, for integrating the track and classification information from an incoming video image stream. We demonstrate the performance of our proposed framework in the application of a real-time vision system for smart automotive airbags. We show that our fusion approach improves the final performance to 100% correct classification, the level required for a robust safety system