Classifiers for Driver Activity Monitoring

The goal of this work is the detection and classification of driver activities in an automobile using computer vision. To this end, this paper presents a novel two-step classification algorithm, namely, an unsupervised clustering algorithm for grouping the actions of a driver during a certain period of time, followed by a supervised activity classification algorithm. The main contribution of this work is the combination of the two methods to provide a computationally fast solution for deployment in real-world scenarios that is robust to illumination and segmentation issues under most conditions experienced in the automobile environment. The unsupervised clustering groups the actions of the driver based on the relative motion detected using a skin-color segmentation algorithm, while the activity classifier is a binary Bayesian eigenimage classifier. Activities are grouped as safe or unsafe and the results of the classification are shown on several subjects obtained from two distinct driving video sequences.

• Record URL:
  • http://www.sciencedirect.com/science/article/B6VGJ-4N9MYH5-1/2/bf37bd85dcee8f0388e7a075b1c4ca7d
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • Abstract reprinted with permission from Elsevier
• Authors:
  • Veeraraghavan, Harini
  • Bird, Nathaniel
  • Atev, Stefan
  • Papanikolopoulos, Nikolaos
• Publication Date: 2007-2

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 51-67
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 15
  • Issue Number: 1
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Algorithms; Behavior; Classification; Computer vision; Detection and identification systems; Drivers; Driving; Monitoring
• Subject Areas: Highways; Safety and Human Factors; I83: Accidents and the Human Factor;

Filing Info

• Accession Number: 01049731
• Record Type: Publication
• Files: TRIS, ATRI
• Created Date: May 25 2007 10:34AM