Classifiers for driver activity monitoring

Harini Veeraraghavan, Nathaniel Bird, Stefan Atev, Nikolaos Papanikolopoulos

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)51-67
Number of pages17
JournalTransportation Research Part C: Emerging Technologies
Volume15
Issue number1
DOIs
StatePublished - Feb 2007

Bibliographical note

Funding Information:
This work was supported by the National Science Foundation through grant #IIS-0219863, the ITS Institute at the University of Minnesota, and the Minnesota Department of Transportation. The authors would also like to thank the subjects for participating in the experiments, as well as the anonymous reviewers for their helpful comments.

Keywords

  • Activity classification
  • Activity history images
  • Driver activity
  • Eigenimage classification
  • Unsupervised clustering

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