Vehicle as a diagnostic space: action recognition while driving a car

Thomas Jacob; Maike Krips; Thomas M. Deserno

doi:10.1117/12.2612611

4 April 2022 Vehicle as a diagnostic space: action recognition while driving a car

Thomas Jacob, Maike Krips, Thomas M. Deserno

Proceedings Volume 12037, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications; 1203707 (2022) https://doi.org/10.1117/12.2612611
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

A person spends a significant portion of time driving a vehicle. This time serves several applications, such as unobtrusive health monitoring with sensors that are mounted inside the car. Such a car can perform regular medical checkups or other tasks such as drunk driver detection. For such tasks, driver behavior monitoring is essential. Several approaches utilize data from different modalities and sensors. Video-based recognition is used increasingly and usually combined with deep learning. In this work, we propose an end-to-end transfer learning approach using temporal pyramidal networks (TPN’s) on top of a ResNet-50 backbone that is pre-trained on the Kinetics400 dataset. We further perform a comparative analysis with the inflated 3D ConvNet network (I3D). We aim to boost training efficiency while improving accuracy as compared to previous work. The extracted videos from the DriveAct dataset have been captured from a single near-infrared (NIR) camera mounted on the rear-view mirror. Using these videos for training and evaluation, we achieve the best validation accuracy of 75.74%. This work has several potentials to be extended, generalizing to a multi-camera setup and combining multi-modal data to increase accuracy significantly. It further serves as a baseline for in-car health monitoring.

Conference Presentation

Citation Download Citation

Thomas Jacob, Maike Krips, and Thomas M. Deserno "Vehicle as a diagnostic space: action recognition while driving a car", Proc. SPIE 12037, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications, 1203707 (4 April 2022); https://doi.org/10.1117/12.2612611

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