A2A | Drowsy Driving | Machine Learning (ML) | Driver Status Information

Common phenomena that appear in the drowsiness stage include eye closure and yawning, and in inattentive situations, the face direction is generally not looking forward. In order to learn these features, we provide a dataset annotating the feature points of the human face, and we also provide images of the driver captured under various conditions to continuously monitor the driver's face to detect facial features and recognize changes to detect drowsiness. We want to build a learning data set to determine the state of carelessness. The driver's facial information is used without de-identification as data captured by a near-infrared camera. Provided: Real Drive Data - Real Environment Data; Semi-Controlled Environment Data; Controlled Environment Data

搜集汇总

背景与挑战

背景概述

该数据集用于检测驾驶员疲劳和分心状态，通过近红外摄像头采集不同环境下的面部图像，并标注面部特征点以支持机器学习。数据涵盖真实驾驶、半控制和受控环境，旨在构建识别驾驶员状态的学习模型。