Information Engineering School, Chang'an University, Xi'an 710061, China.
Department of Computer Science and IT, The University of Agriculture Peshawar, Peshawar 25000, Pakistan.
Sensors (Basel). 2022 May 23;22(10):3959. doi: 10.3390/s22103959.
Car crashes are among the top ten leading causes of death; they could mainly be attributed to distracted drivers. An advanced driver-assistance technique (ADAT) is a procedure that can notify the driver about a dangerous scenario, reduce traffic crashes, and improve road safety. The main contribution of this work involved utilizing the driver's attention to build an efficient ADAT. To obtain this "attention value", the gaze tracking method is proposed. The gaze direction of the driver is critical toward understanding/discerning fatal distractions, pertaining to when it is obligatory to notify the driver about the risks on the road. A real-time gaze tracking system is proposed in this paper for the development of an ADAT that obtains and communicates the gaze information of the driver. The developed ADAT system detects various head poses of the driver and estimates eye gaze directions, which play important roles in assisting the driver and avoiding any unwanted circumstances. The first (and more significant) task in this research work involved the development of a benchmark image dataset consisting of head poses and horizontal and vertical direction gazes of the driver's eyes. To detect the driver's face accurately and efficiently, the You Only Look Once (YOLO-V4) face detector was used by modifying it with the Inception-v3 CNN model for robust feature learning and improved face detection. Finally, transfer learning in the InceptionResNet-v2 CNN model was performed, where the CNN was used as a classification model for head pose detection and eye gaze angle estimation; a regression layer to the InceptionResNet-v2 CNN was added instead of SoftMax and the classification output layer. The proposed model detects and estimates head pose directions and eye directions with higher accuracy. The average accuracy achieved by the head pose detection system was 91%; the model achieved a RMSE of 2.68 for vertical and 3.61 for horizontal eye gaze estimations.
车祸是十大主要死亡原因之一；它们主要可归因于分心驾驶。先进驾驶辅助技术 (ADAT) 是一种可以通知驾驶员危险情况、减少交通事故和提高道路安全的程序。这项工作的主要贡献涉及利用驾驶员的注意力来构建高效的 ADAT。为了获得这个“注意力值”，提出了视线追踪方法。驾驶员的注视方向对于理解/辨别致命干扰至关重要，这与何时必须通知驾驶员道路上的风险有关。本文提出了一种实时视线追踪系统，用于开发获取和传达驾驶员视线信息的 ADAT。所开发的 ADAT 系统检测驾驶员的各种头部姿势并估计眼睛注视方向，这在协助驾驶员和避免任何意外情况方面发挥着重要作用。这项研究工作的第一个（也是更重要的）任务是开发一个基准图像数据集，该数据集包含驾驶员的头部姿势和水平及垂直方向的眼睛注视。为了准确高效地检测驾驶员的面部，使用了仅看一次（YOLO-V4）面部探测器，并通过使用 Inception-v3 CNN 模型对其进行修改，以进行稳健的特征学习和改进的面部检测。最后，在 InceptionResNet-v2 CNN 模型中进行了迁移学习，其中将 CNN 用作分类模型，用于头部姿势检测和眼睛注视角度估计；将回归层添加到 InceptionResNet-v2 CNN 中，而不是 SoftMax 和分类输出层。所提出的模型以更高的精度检测和估计头部姿势方向和眼睛方向。头部姿势检测系统的平均准确率为 91%；该模型在垂直方向上的 RMSE 为 2.68，在水平方向上的 RMSE 为 3.61。
