Driver-car Natural Interaction Method Based on Head-Eye Behaviors
Haojie Li, Qijie Zhao, Weichi Zhao, Yijing Wu · 2019
When drivers interact with the car, their hands will leave the steering wheel, causing great safety risks. In order to solve this problem, this paper proposed a natural interaction method based on driver's head-eye moving behaviors by capturing driver's image and detecting multi-feature in driver's face. Firstly, 2D images of the driver's head and 3D point cloud data are collected through the infrared camera installed in front of the driver in the car. By using the SDM minimization nonlinear least square function to detect the human face in 2D infrared images, the features of human face are obtained. And the eye area images are obtained by using the feature point coordinates. The C-V extension model based on feature search was used to extract the pupil and the feature points of the Purkinje image in the eye image. Meanwhile, the spatial coordinates of the points in the three-dimensional point cloud were transformed by using the index of facial feature points to obtain the driver's head posture. Finally, the decision tree model is used to analyze the obtained eye feature vectors and head posture data, by this way, mapping these head-eye behaviors to the gazing object, and driver can use it to interaction with car. The experimental results show that the proposed method in this paper can accurately and quickly identify the driver's interaction intentions on the vehicle.