基于机器视觉的协同车辆基础设施系统

Daxin Tian, Chuang Zhang, Xuting Duan, Jianshan Zhou, Zhengguo Sheng, Victor C. M. Leung
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引用次数: 7

摘要

信息采集是协同车载基础设施系统(CVIS)的关键环节。随着计算机图像处理技术的进步,越来越多的研究人员将图像识别作为信息获取的来源。在此背景下,作者开发了一个基于机器视觉的CVIS系统,包括车辆子系统、路边子系统和停车场子系统。该系统采用改进的Canny算法检测道路通道化,采用HOG+SVM方法检测行人,采用Haar+Adaboost方法检测车辆。实验结果表明,该系统具有较高的检测精度和实时性。此外,测试也证明了该系统在驾驶辅助方面的显著作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Cooperative Vehicle Infrastructure System Based on Machine Vision
The information acquisition is a key procedure of cooperative vehicle-infrastructure system (CVIS). With the advancement of computer image processing technology, more and more researchers use image recognition as the source of information acquisition. On this background, the authors develop a CVIS based on machine vision, including vehicular subsystem, the roadside subsystem and the parking lot subsystem. The system uses improved Canny algorithm to detect road channelization, HOG+SVM method to detect pedestrian and Haar+Adaboost method to detect vehicle. The experiment result shows that the detection accuracy and real-time of system is relatively high. In addition, the test also prove that the system is significant in driving assistance.
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