Nonparametric Approaches for Estimating Driver Pose

作者：Paul Watta, Sridhar Lakshmanan, Yulin Hou · 发表于：IEEE Transactions on Vehicular Technology · 年份：2007 · DOI：10.1109/tvt.2007.897634 · 被引用次数：24 · 研究领域：Video Surveillance and Tracking Methods、Autonomous Vehicle Technology and Safety、Anomaly Detection Techniques and Applications

To better understand driver behavior, the Federal Highway Administration and the National Highway Traffic Safety Administration have collected several thousands of hours of driver video. There is now an immediate need for devising automated procedures for analyzing the video. In this paper, we look at the problem of estimating driver pose given a video of the driver as he or she drives the vehicle. A complete system is proposed to perform feature extraction and classification of each frame. The system uses a Fisherface representation of video frames and a nearest neighbor and neural network classification scheme. Experimental results show that the system can achieve high accuracy and reliable performance.