摘要
在现实生活中,因人流量过大而引发的安全事故不胜枚举.为了防止此类事故的发生,可通过视频监控的方式统计人数,及时对行人进行限流和分流.提出一种有效的人数统计算法.该算法采用深度摄像机作为视频采集源,通过分析和提取深度图像下头部的4个特征,实现行人头部检测,并依靠Kalman滤波技术实现对头部目标的跟踪,进而达到人数统计的目的.该算法对行人的不同发型具有一定适应性,同时对轻微遮挡和多人环境下的头部检测均有良好效果.该算法人数统计平均准确率达到88.6%.
In daily life,a great number of security accidents are caused by the excessive flow of people. In order to prevent the occurrence of such accidents,we propose an efficient algorithm to count the number of people by using video monitors and limit the flow of people in time. The algorithm uses the depth camera as a video capture device and realizes the detection of people＇s heads by analyzing and extracting the four features of heads in depth image.The method uses Kalman filter technology to track the head and achieves the purpose of counting statistics. The proposed algorithm can effectively solve the head detection problem of complex scenes,such as hairstyle diversity and head part-occlusion. The average accuracy of the proposed algorithm reaches about 88. 6%.
出处
《深圳大学学报（理工版）》
EI
CAS
CSCD
北大核心
2017年第6期584-590,共7页
Journal of Shenzhen University(Science and Engineering)
基金
广东省自然科学基金资助项目(2015A030310172)
深圳市科技计划资助项目(JCYJ20170302145623566
JCYJ20160331185006518)~~
