Driver activity monitoring through supervised and unsupervised learning
Driver activity monitoring through supervised and unsupervised learning
复制标题
DOI：
10.1109/itsc.2005.1520169
复制
发表时间：
2005-10
期刊：
影响因子：
--
通讯作者：
H. Veeraraghavan;Stefan Atev;Nathaniel D. Bird;P. Schrater;N. Papanikolopoulos
中科院分区：
文献类型：
--
作者：
H. Veeraraghavan;Stefan Atev;Nathaniel D. Bird;P. Schrater;N. Papanikolopoulos
本文提出了两种应用于驾驶员活动监控任务的不同学习方法。这些方法的目标是检测驾驶员不安全活动的时期，例如打电话、吃东西或调整仪表板无线电系统。这里介绍的系统使用侧面安装的摄像头来查看驾驶员的个人资料，并利用从肤色分割获得的轮廓外观来检测活动。无监督方法使用凝聚聚类来简洁地表示整个序列中的驾驶员活动，而监督学习方法使用贝叶斯特征图像分类器来区分活动。介绍并广泛讨论了应用于三个不同主题的驾驶序列的两种学习方法的结果。
This paper presents two different learning methods applied to the task of driver activity monitoring. The goal of the methods is to detect periods of driver activity that are not safe, such as talking on a cellular telephone, eating, or adjusting the dashboard radio system. The system presented here uses a side-mounted camera looking at a driver's profile and utilizes the silhouette appearance obtained from skin-color segmentation for detecting the activities. The unsupervised method uses agglomerative clustering to succinctly represent driver activities throughout a sequence, while the supervised learning method uses a Bayesian eigen-image classifier to distinguish between activities. The results of the two learning methods applied to driving sequences on three different subjects are presented and extensively discussed.