Crowd density estimation based on statistical analysis of local intra-crowd motions for public area surveillance

Zhaoxiang Zhang; Min Li

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Crowd density estimation based on statistical analysis of local intra-crowd motions for public area surveillance

机译：Crowd density estimation based on statistical analysis of local intra-crowd motions for public area surveillance

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摘要

Crowd density estimation in public areas with people gathering and waiting has been a challenging problem for visual surveillance over many years. Tiny motions, like when people turn around, wander about, and turn their heads, happen randomly now and then in crowds, which makes it difficult to achieve high-performance crowd density estimation based on traditional foreground detection. A novel accumulated mosaic image difference feature is proposed to represent these complicated random motion patterns for accurate foreground detection. The obtained foreground is then normalized based on the perspective distortion correction model to achieve a reasonable crowd density measurement for observed areas. Numerous experiments are conducted in different scenes of various view angles, and experimental results demonstrate the effectiveness and robustness of our proposed method.

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来源
《Optical Engineering》 |2012年第4期|047204-1-047204-13|共13页
作者
Zhaoxiang Zhang; Min Li;
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作者单位

Beihang University, Laboratory of Intelligent Recognition and Image Processing, Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beijing 100191, China;

Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing 100190, China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类计量学;
关键词
visual surveillance; crowd density analysis; motion detection; perspective distortion normalization;

Crowd density estimation based on statistical analysis of local intra-crowd motions for public area surveillance

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