University of Minnesota Driven to Discover
U of MNUniversity of Minnesota
Center for Transportation Studies

Find Reports


Related Links

Finding What the Driver Does

Harini Veeraraghavan, Stefan Atev, Nathaniel Bird, Paul Schrater, Nikolaos P. Papanikolopoulos
May 2005
Report no. CTS 05-03


Most research depends on detection of driver alertness through monitoring the eyes, face, head or facial expression. This research presents methods for recognizing and summarizing the activities of drivers using the appearance of the driver's position, and changes in position, as fundamental cues, based on the assumption that periods of safe driving are periods of limited motion in the driver's body. The system uses a side-mounted camera and utilizes silhouettes obtained from skin color segmentation for detecting activities. The unsupervised method uses agglomerative clustering to represent driver activity throughout a sequence, while the supervised learning method uses a Bayesian eigen image classifier to distinguish between activities. The results validate the advantages of using driver appearance obtained from skin color segmentation for classification and clustering purposes. Advantages include increased robustness to illumination variations and elimination of the need for tracking and pose determination.

Download or Order

Download PDF (940 KB)
For print version, view order form or contact CTS Library
Sponsored by: ITS Institute (RITA)