Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Driver activity monitoring through supervised and unsupervised learning
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.
Driver activity monitoring through supervised and unsupervised learning
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.
Driver activity monitoring through supervised and unsupervised learning
Veeraraghavan, H. (Autor:in) / Atev, S. (Autor:in) / Bird, N. (Autor:in) / Schrater, P. (Autor:in) / Papanikolopoulos, N. (Autor:in)
01.01.2005
300789 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Driver Activity Monitoring through Supervised and Unsupervised Learning
| British Library Conference Proceedings | 2005
Supervised vs. unsupervised learning for construction crew productivity prediction
| British Library Online Contents | 2012
Supervised vs. unsupervised learning for construction crew productivity prediction
| Online Contents | 2012
| Taylor & Francis Verlag | 2023
Short-term water demand forecasting using hybrid supervised and unsupervised machine learning model
| Springer Verlag | 2020