Driver's distraction is one of the main areas, which researchers are focusing on, in design of Advanced Drivers Assistance Systems (ADASs). Head pose and eye-gaze direction are two reliable indicators of a driver's gaze and the current focus of attention. Compared with other methods that make use of head pose only, methods that combine eye information can achieve higher accuracy. The naturalistic driving environment always presents unique challenges (e.g., unstable illumination, jolts, etc.) to video-based gaze estimation and tracking systems. Some methods can achieve relatively high proficiency in the stationary laboratory environment, but they may not be suitable for the unstable driving environment. In addition, performing in real time or near-real time is another consideration for gaze estimation in an ADAS. Therefore, these special challenges need to be overcome to design ADASs.
In this thesis, we proposed a new driver's gaze zone estimation framework designed for the naturalistic driving environment. The framework combines head and eye information to estimate the gaze zone of the driver in both daytime and nighttime. The framework is composed of five main components: Facial Landmark Detection, Head Pose Estimation, Iris Center Detection, Upper Eyelid Information Extraction, and Gaze Zone Estimation. First, Constrained Local Neural Field (CLNF) is applied to obtain the facial landmarks in the image plane and the 3D model of the face in the object frame. In addition, extracting region of interest (ROI) is utilized as an optimization strategy for CLNF facial landmark detection. Second, head pose estimation can be regarded as a Perspective-n-Point (PnP) problem. Levenberg-Marquardt optimization method is used to solve the PnP problem based on the 2D landmark locations in the image plane and their corresponding 3D locations in the object frame. Third, a regression model-based method is employed to obtain the iris center from eye landmarks detected in the previous part. For upper eyelid information extraction, a quadratic function is utilized to model the upper eyelid, and the second-order coefficient is extracted. Finally, the head pose and the eye information are combined to form a feature vector, and Random Decision Forest classifier is utilized to estimate the current gaze zone of the driver from the feature vector extracted.
The experiment is carried out in the realistic driving environment in both daytime and nighttime with three volunteers by Kinect sensor V2 for Windows that is put at the back of windshield. Weighted and unweighted accuracy are utilized as evaluation metrics in gaze zone estimation. Weighted accuracy evaluates gaze zones with different significance while unweighted accuracy treats each gaze zone equally. Experiment results show that the gaze zone estimation framework proposed in this work has better performance compared to the reference in the daytime. The weighted and unweighted accuracy of gaze zone estimation reach 96.6% and 95.0% for daytime, respectively. For nighttime, the weighted and unweighted accuracy can reach 96% and 91.4%.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/38076 |
| Date | 07 September 2018 |
| Creators | Luo, Chong |
| Contributors | Boukerche, Azzedine |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.0024 seconds