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Real-time pose classification for driver monitoring

Xia Liu, / Youding Zhu, / Fujimura, K.

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.

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Real-time pose classification for driver monitoring

Xia Liu, / Youding Zhu, / Fujimura, K.

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

Xia Liu, / Youding Zhu, / Fujimura, K.

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.

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Title:

Real-time pose classification for driver monitoring

Contributors:

Xia Liu, (author) / Youding Zhu, (author) / Fujimura, K. (author)

Published in:

Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems ; 174-178

Publication date:

2002-01-01

Size:

451709 byte

ISBN:

DOI:

https://doi.org/10.1109/ITSC.2002.1041209

Type of media:

Conference paper

Type of material:

Electronic Resource

Language:

English

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