文章基本信息

标题：DriverMVT: In-Cabin Dataset for Driver Monitoring including Video and Vehicle Telemetry Information
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作者：Walaa Othman ; Alexey Kashevnik ; Ammar Ali 等
期刊名称：Data
印刷版ISSN：2306-5729
出版年度：2022
卷号：7
期号：5
页码：1-13
DOI：10.3390/data7050062
语种：English
出版社：MDPI Publishing
摘要：Developing a driver monitoring system that can assess the driver’s state is a prerequisite and a key to improving the road safety. With the success of deep learning, such systems can achieve a high accuracy if corresponding high-quality datasets are available. In this paper, we introduceDriverMVT (Driver Monitoring dataset with Videos and Telemetry). The dataset contains information about the driver head pose, heart rate, and driver behaviour inside the cabin like drowsiness and unfastened belt. This dataset can be used to train and evaluate deep learning models to estimate the driver’s health state, mental state, concentration level, and his/her activity in the cabin. Developing such systems that can alert the driver in case of drowsiness or distraction can reduce the number of accidents and increase the safety on the road. The dataset contains 1506 videos for 9 different drivers (7 males and 2 females) with total number of frames equal 5119k and total time over 36 h. In addition, evaluated the dataset with multi-task temporal shift convolutional attention network (MTTS-CAN) algorithm. The algorithm mean average error on our dataset is 16.375 heartbeats per minute.
关键词：driver monitoring dataset;driver state;driver activity recognition;driver drowsiness detection