DriverMVT: In-Cabin Dataset for Driver Monitoring including Video and Vehicle Telemetry Information

• Published in: Data (Basel) Vol. 7; no. 5; p. 62
• Main Authors: Othman, Walaa, Kashevnik, Alexey, Ali, Ammar, Shilov, Nikolay
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Algorithms; Automation; Behavior; Blood pressure; Cameras; Datasets; Deep learning; driver activity recognition; Driver behavior; driver drowsiness detection; driver monitoring dataset; driver state; Evaluation; Fatigue; Fuzzy sets; Global positioning systems; GPS; Heart rate; Machine learning; Maximum likelihood method; Monitoring; Researchers; Roads & highways; Sensors; Sleepiness; Telemetry; Traffic; Traffic safety; Video
• ISSN: 2306-5729; 2306-5729
• DOI: 10.3390/data7050062

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 introduce DriverMVT (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.