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计算机系统应用英文版:2020,29(4):220-225
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基于角点特征分析的车辆排队长度检测方法
(中国石油大学(华东) 计算机科学与技术学院, 青岛 266580)
Vehicle Queue Length Detection Method Based on Corner Feature Analysis
(College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China)
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Received:August 08, 2019    Revised:September 05, 2019
中文摘要: 针对交通堵塞造成的各种状况,通过视频分析实现实时高效的车辆排队长度检测,从而获取更多的交通信息改善交通状况.本文通过传统的FAST角点检测方法与运动检测的过程相结合得到改进后的FAST算法,使用改进后的FAST角点特征分析技术,不仅可以提取出当前交通道路上表征车辆存在的角点特征图,还可以获取角点位置的运动状态.通过对交通监控下的视频进行预处理后,单一车道内处于静态的角点特征形成车辆排队,并进行PCA处理得到一维向量,最后对一维向量进行形态学处理来检测单一车道内的车辆排队长度.实验表明,本方法检测精度平均98%,满足应用于实际场景.
中文关键词: 运动检测  改进FAST  PCA  车辆排队  形态学处理
Abstract:In view of various situations caused by traffic jam, video analysis is used to realize real-time and efficient detection of vehicle queue length, so as to obtain more traffic information and improve traffic conditions. In this study, the improved FAST algorithm is obtained by combining the traditional FAST corner detection method with the process of motion detection. By using the improved FAST corner feature analysis technology, not only can the corner feature graph representing the presence of vehicles on the current traffic road be extracted, but also can the motion state of corner position be obtained. After the pretreatment of video under traffic monitoring, the static corner point features in a single lane form vehicle queuing, and PCA processing is carried out to obtain one-dimensional vector. Finally, morphological processing is carried out to detect the queue length of vehicles in a single lane. The experimental results show that the detection accuracy of this method is 98% on average, which can be applied to the actual scene.
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基金项目:中央高校基本科研业务费专项资金(19CX02030A)
引用文本:
刘新平,张影,王风华.基于角点特征分析的车辆排队长度检测方法.计算机系统应用,2020,29(4):220-225
LIU Xin-Ping,ZHANG Ying,WANG Feng-Hua.Vehicle Queue Length Detection Method Based on Corner Feature Analysis.COMPUTER SYSTEMS APPLICATIONS,2020,29(4):220-225