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| DOI：10.1007/s11768-010-8043-0 |
| Received:March 20, 2008Revised:March 12, 2009 |
| 基金项目:The National Natural Science Foundation of China |
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| A new real-time eye tracking based on nonlinear Unscented Kalman Filter for monitoring driver fatigue |
| Zutao ZHANG,Jiashu ZHANG |
| (School of Mechanical Engineering, Southwest Jiaotong University) |
| Abstract: |
| A new scheme for driver fatigue detection is presented, which is based on the nonlinear unscented Kalman
filter and eye tracking. Assuming a probability distribution than to approximate an arbitrary nonlinear function or transformation,
eye nonlinear tracking can be achieved using an unscented transformation (UT), which adopts a set of deterministic
sigma points to match the posterior probability density function of the eye movement. Driver fatigue can be detected using
the percentage of eye closure (PERCLOS) framework in a realistic driving condition after the eye nonlinear tracking. This
system was tested adequately in realistic driving environments with subjects of different genders, with/without glasses,
in day/night driving, being commercial/noncommercial drivers, in continuous driving time, and under different road conditions.
The last experimental results show that the proposed method not only improves the robustness for nonlinear eye
tracking, but also can provide more accurate estimation than the traditional Kalman filter. |
| Key words: Eye tracking Unscented Kalman Filter (UKF) Fatigue detection PERCLOS |
