This paper presents a vision-based real-time gaze zone estimator based on a driver's head orientation composed of yaw and pitch. Generally, vision-based methods are vulnerable to the wearing of eyeglasses and image variations between day and night. The proposed method is novel in the following four ways: First, the proposed method can work under both day and night conditions and is robust to facial image variation caused by eyeglasses because it only requires simple facial features and not specific features such as eyes, lip corners, and facial contours. Second, an ellipsoidal face model is proposed instead of a cylindrical face model to exactly determine a driver's yaw. Third, we propose new featuresthe normalized mean and the standard deviation of the horizontal edge projection histogramto reliably and rapidly estimate a driver's pitch. Fourth, the proposed method obtains an accurate gaze zone by using a support vector machine. Experimental results from 200000 images showed that the root mean square errors of the estimated yaw and pitch angles are below 7 under both daylight and nighttime conditions. Equivalent results were obtained for drivers with glasses or sunglasses, and 18 gaze zones were accurately estimated using the proposed gaze estimation method.
|Number of pages||14|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Publication status||Published - 2011 Mar|
Bibliographical noteFunding Information:
Manuscript received June 23, 2009; revised January 22, 2010 and September 30, 2010; accepted October 23, 2010. Date of publication January 17, 2011; date of current version March 3, 2011. This work was supported in part by Mando Corporation Ltd. and in part by the National Research Foundation of Korea through the Biometrics Engineering Research Center, Yonsei University, under Grant R112002105070030(2010). The Associate Editor for this paper was A. Amditis.
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications