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

Walaa Othman, Alexey Kashevnik, Ammar Ali and Nikolay Shilov
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Walaa Othman: Information Technology and Programming Faculty, ITMO University, 197101 St. Petersburg, Russia
Alexey Kashevnik: Information Technology and Programming Faculty, ITMO University, 197101 St. Petersburg, Russia
Ammar Ali: Information Technology and Programming Faculty, ITMO University, 197101 St. Petersburg, Russia
Nikolay Shilov: St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia

Data, 2022, vol. 7, issue 5, 1-13

Abstract: 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.

Keywords: driver monitoring dataset; driver state; driver activity recognition; driver drowsiness detection (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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