Journal
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 15, Issue 1, Pages 51-67Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2007.01.001
Keywords
driver activity; unsupervised clustering; activity history iniages; eigenimage classification; activity classification
Categories
Ask authors/readers for more resources
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. (C) 2007 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available