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 language | English (US) |
|---|---|
| Pages (from-to) | 51-67 |
| Number of pages | 17 |
| Journal | Transportation Research Part C: Emerging Technologies |
| Volume | 15 |
| Issue number | 1 |
| DOIs | |
| State | Published - 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