摘要
车辆检测是智能交通中的一个基本问题。为了有效地检测车辆,本文提出了基于多通道背景提取算法的车辆检测方法。首先采用多通道灰度化预处理图像,并分别建立相应的背景模型,接着衡量新像素点和背景模型的相似性,分类出前景和背景像素,然后运用形态学操作和区域标记法滤除噪声,检测出运动目标,并通过区域标记定位目标。本文从不同角度开展对比实验,结果表明基于多通道背景提取算法的车辆检测能够有效提高车辆检测的精确性和完整性。
Vehicle detection is a basic problem in intelligent transportation. To effectively detect vehicles, this paper proposes a vehicle detection method based on multi-channel background extraction algorithm. Firstly, the multi-channel gray-scale preprocessing images are used to establish the corresponding background model.Secondly, the similarity between the new pixel and the background model is measured, classifying the foreground and the background pixels. Finally, our method filters the noise by the morphological and regional markers, detects the moving target, and locates the target through the area marker. In this paper, extensive experiments are carried out. The experimental results show that vehicle detection based on multi-channel background extraction algorithm can effectively improve the accuracy and completeness of vehicle detection.
出处
《电脑知识与技术》
2018年第4Z期203-206,共4页
Computer Knowledge and Technology
基金
国家自然科学基金上面项目(61671255)
江苏省高校自然科学研究上面项目(16KJB520036)
公安部技术研究计划项目(2016JSYJB51)
江苏省物联网移动互联技术工程实验室开放课题(JSWLW-2017-015)
江苏省社会安全图像与视频理解重点实验室创新基金编号(30916014107)
南通市科技项目前沿与关键技术(MS22015100)
南通大学自然科学基金(13040501)
南通大学大学生创新创业训练计划项目"车让人背景下行人和车辆轨迹分析系统研究"
关键词
车辆检测
多通道
前景融合
区域标记法
Vehicle detection
multi-channel
Foreground fusion
area marking method
