CUED Publications database

Automatic detection of a driver’s complex mental states

Ma, Z and Mahmoud, M and Robinson, P and Dias, E and Skrypchuk, L (2017) Automatic detection of a driver’s complex mental states. In: International Conference on Computational Science and its Applications, 2017-7-3 to -- pp. 679-691..

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Automatic classification of drivers’ mental states is an important yet relatively unexplored topic. In this paper, we define a taxonomy of a set of complex mental states that are relevant to driving, namely: Happy, Bothered, Concentrated and Confused. We present our video segmentation and annotation methodology of a spontaneous dataset of natural driving videos from 10 different drivers. We also present our real-time annotation tool used for labelling the dataset via an emotion perception experiment and discuss the challenges faced in obtaining the ground truth labels. Finally, we present a methodology for automatic classification of drivers’ mental states. We compare SVM models trained on our dataset with an existing nearest neighbour model pre-trained on posed dataset, using facial Action Units as input features. We demonstrate that our temporal SVM approach yields better results. The dataset’s extracted features and validated emotion labels, together with the annotation tool, will be made available to the research community.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div C > Engineering Design
Div C > Materials Engineering
Depositing User: Cron Job
Date Deposited: 19 Aug 2017 20:04
Last Modified: 10 Apr 2021 00:47
DOI: 10.1007/978-3-319-62398-6_48