Driver pose estimation is one of the key components for future driver assistance systems since driver pose contains much information about his driving condition such as attention and fatigue levels. To this goal, a system is presented that detects the pose of the driver face in real time under realistic lighting conditions. The goal of the work is to automate the training phase, thereby eliminating the process of entering user information as much as possible. Two learning methods are presented for driver pose estimation. The first method uses unsupervised learning with Kohonen competitive networks, while the second method explores SVR with an appearance-based method.
Real-time pose classification for driver monitoring
2002-01-01
451709 byte
Conference paper
Electronic Resource
English
REAL-TIME VIEW CLASSIFICATION FOR DRIVER MONITORING
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