Journal of Software:2014.25(S2):258-267

(中山大学 信息科学与技术学院, 广东 广州 510006;安徽大学 计算机科学与技术学院, 安徽 合肥 230601)
Counting Pedestrians in High-Density Crowd Scenes Using Cross-Sectional Flow Statistics
JI Qing-Ge,CHEN Jing,CHI Rui,FANG Xian-Yong
(School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, China;School of Computer Science and Technology, Anhui University, Hefei 230601, China)
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Received:May 09, 2014    Revised:August 19, 2014
> 中文摘要: 利用摄像头实现行人计数在智能视频监控领域有着重要的价值,但是行人互相遮挡、噪声、摄像机透视效果和图像背景等问题影响了人群计数的准确性.针对高密度人群场景的行人计数准确率的问题,提出了基于截面流量统计的行人计数方法,该方法基于梯度运动历史图像检测前景,并用有效运动图像改进了基于特征提取的行人计数方法,结合运动速度提取方法实现了行人计数.实验结果表明,提出的计数方法在高密度人群场景中具有较高的准确率和实时性,是一种针对高密度人群有效的行人计数方法.
Abstract:Surveillance cameras have been widely installed in cities all over the world in recent years. Counting pedestrians from cameras has become a very important issue in intelligent video surveillance. However, factors such as occlusions, noise, camera perspective, background clutter may affect the accuracy of pedestrian counting. This paper introduces a pedestrian counting method for high-density crowd scenes using cross-sectional flow statistics. The proposed method consists of a new foreground detection algorithm based on the gradient motion history image, an improved feature-based counting algorithm by an effective motion image, and a moving speed extraction algorithm using optical flows. The experimental results show that the proposed method is robust and effective for counting pedestrians.
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基金项目:国家自然科学基金(U0735001,60473109,61003131);广东省自然科学博士启动基金(04300602);安徽省自然科学基金(1408085MF113) 国家自然科学基金(U0735001,60473109,61003131);广东省自然科学博士启动基金(04300602);安徽省自然科学基金(1408085MF113)
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JI Qing-Ge,CHEN Jing,CHI Rui,FANG Xian-Yong.Counting Pedestrians in High-Density Crowd Scenes Using Cross-Sectional Flow Statistics.Journal of Software,2014,25(S2):258-267