基于SSD方法的驾驶员非法驾驶行为检测

Tao Yang, Jin Yang, Jicheng Meng
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引用次数: 0

摘要

本文提出了一种基于深度学习的单镜头多盒检测器(Single Shot MultiBox Detector, SSD)的高级违章驾驶行为检测方法。对驾驶员非法驾驶行为的检测包括使用手机、吸烟和未系安全带。这样做可以大大减少交通事故的发生。为了验证SSD在复杂环境下对香烟等小目标物体的检测效果,我们不仅使用了HMDB人体运动数据库、WIDER FACE数据库、Hollywood-2数据库这三个在线数据库,而且还使用了我们自己采集的真实数据库。实验结果表明,SSD方法在检测驾驶员违章驾驶行为方面比Faster - R-CNN方法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Driver’s Illegal Driving Behavior Detection with SSD Approach
In this paper, an advanced detection approach of illegal driving behavior is proposed using Single Shot MultiBox Detector (SSD) based on deep learning. The detection of driver’s illegal driving behavior includes cellphone usage, cigarette smoke and no fastening seat belt. Doing this can greatly reduce the occurrence of traffic accidents. In order to validate the detection effect using SSD on small target objects, such as cigarette in complex environment, we use not only three online databases, i.e. HMDB human motion database, WIDER FACE Database, Hollywood-2 Database, but also a real database collected by ourselves. The experimental results show that the SSD approach has a better performance than the Faster Regions with Convolutional Neural Network (Faster R-CNN) for detecting driver’s illegal driving behavior.
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