Particularly in the automotive environment where standard input devices such as the mouse and keyboard are impractical, gesture recognition holds the promise of making man-machine interaction more natural, intuitive and safe [5]. But especially in a dynamic environment like the car, visionbased classification of gestures is a challenging problem. This thesis compares a probabilistic and a rulebased approach to classify 17 different hand gestures in an automotive environment and proposes new methods how to integrate external sensor information into the recognition process. In the first part of the thesis, different techniques in extracting the hand region out of the video stream are presented and compared with regard to robustness and performance. The second part of the thesis compares a HMM-based approach by Morguet [1] and a hierarchical approach by Mammen [2] to recognize gestures and the integration of external context knowledge into the classification process. The final system achieves person independent recognition rates of 86 percent in the desktop and 76 percent in the automotive environment.     «

Particularly in the automotive environment where standard input devices such as the mouse and keyboard are impractical, gesture recognition holds the promise of making man-machine interaction more natural, intuitive and safe [5]. But especially in a dynamic environment like the car, visionbased classification of gestures is a challenging problem. This thesis compares a probabilistic and a rulebased approach to classify 17 different hand gestures in an automotive environment and proposes new meth...     »