[1]刘磊,陈泽虹,张勇,等.深度图像下基于头部多特征的人数统计算法[J].深圳大学学报理工版,2017,34(No.6(551-660)):589-595.
 Liu Lei,Chen Zehong,Zhang Yong,et al.A people counting algorithm with multi-feature of head region for depth images[J].Journal of Shenzhen University Science and Engineering,2017,34(No.6(551-660)):589-595.
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深度图像下基于头部多特征的人数统计算法()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第34卷
期数:
2017年No.6(551-660)
页码:
589-595
栏目:
电子与信息科学
出版日期:
2017-11-30

文章信息/Info

Title:
A people counting algorithm with multi-feature of head region for depth images
作者:
刘磊1陈泽虹2张勇1赵东宁23
1)深圳大学ATR国防科技重点实验室, 广东深圳518060;2)深圳大学信息工程学院, 广东深圳518060; 3) 哈尔滨工业大学(深圳),广东深圳 518055
Author(s):
Liu Lei1 Chen Zehong2 Zhang Yong1 and Zhao Dongning23
1) ATR Key Laboratory of National Defense Technology, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China 2) College of Information Engineering, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China 3) Harbin Institute of Technology (Shenzhen), Shenzhen 518055, Guangdong Province, P.R.China
关键词:
图像处理深度图像区域生长Kalman滤波多特征人数统计
Keywords:
Image processing Depth image Region growing Kalman filter Multi-feature People counting
文献标志码:
A
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。本文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四个特征,实现行人头部的检测,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的适应性,同时对于轻微遮挡和多人环境下的头部检测均有良好效果.该算法的人数统计平均准确率能达到88.6%.
Abstract:
Today, a great number of security accidents are caused by the excessive flow of people. To prevent such accidents, we propose an efficient algorithm to count the number of people by video monitors in order to limit the flow of people in time. Our algorithm uses the depth camera as a video capture device. The algorithm realizes the detection of people’s heads by analyzing and extracting the four features of heads in depth image. The method relies on Kalman filter technology to track the head and achieve 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 rate of the algorithm reaches about 88.6%.

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更新日期/Last Update: 2017-10-10