ROBUST MULTIMODAL HAND- AND HEAD GESTURE RECOGNITION FOR CONTROLLING AUTOMOTIVE INFOTAINMENT SYSTEMS

The use of gestures in automotive environments provides an intuitive addition to existing interaction styles for seamlessly controlling various infotainment applications like radio-tuner, cd-player and telephone. In this work, we describe a robust, context-specific approach for a video-based analysis of dynamic hand- and head gestures. The system, implemented in a BMW limousine, evaluates a continuous stream of infrared pictures using a combination of adapted preprocessing methods and a hierarchical, mainly rule based classification scheme. Currently, 17 different hand gestures and six different head gestures can be recognized in realtime on standard hardware. As a key-feature of the system, the active gesture vocabulary can be reduced with regard to the current operating context yielding more robust performance. 1