Automatic detection of a driver’s complex mental states
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Ma, Z., Mahmoud, M., Robinson, P., Dias, E., & Skrypchuk, L. (2017). Automatic detection of a driver’s complex mental states. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10406 LNCS 679-691. https://doi.org/10.1007/978-3-319-62398-6_48
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.
External DOI: https://doi.org/10.1007/978-3-319-62398-6_48
This record's URL: https://www.repository.cam.ac.uk/handle/1810/282964