Abstract

This paper presents two different learning methods applied to the task of driver activity monitoring. The goal of the methods is to detect periods of driver activity that are not safe, such as talking on a cellular telephone, eating, or adjusting the dashboard radio system. The system presented here uses a side-mounted camera looking at a driver's profile and utilizes the silhouette appearance obtained from skin-color segmentation for detecting the activities. The unsupervised method uses agglomerative clustering to succinctly represent driver activities throughout a sequence, while the supervised learning method uses a Bayesian eigen-image classifier to distinguish between activities. The results of the two learning methods applied to driving sequences on three different subjects are presented and extensively discussed.

Keywords

Computer scienceArtificial intelligenceUnsupervised learningSilhouetteCluster analysisClassifier (UML)SegmentationSupervised learningActivity recognitionMachine learningHierarchical clusteringPattern recognition (psychology)Computer visionArtificial neural network

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Publication Info

Year
2005
Type
article
Volume
290
Pages
895-900
Citations
29
Access
Closed

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Harini Veeraraghavan, Stefan Atev, Nathaniel Bird et al. (2005). Driver activity monitoring through supervised and unsupervised learning. , 290 , 895-900. https://doi.org/10.1109/itsc.2005.1520169

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DOI
10.1109/itsc.2005.1520169