Panoramic density estimation method in complex scene

doi:10.11772/j.issn.1001-9081.2014.06.1715

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (6): 1715-1718.DOI: 10.11772/j.issn.1001-9081.2014.06.1715

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Panoramic density estimation method in complex scene

HE Kun¹,LIU Zhou²,WEI Luning¹,YANG Heng²,ZHU Tong¹,LIU Yanwei¹,ZHOU Jimei²

1. VIP Protection Department, Ministry of Public Security of the People's Republic of China, Beijing 100031, China;
2. Beijing Zheng An Rong Han Technology Company Limited, Beijing 100088, China

Received:2013-12-20 Revised:2014-02-12 Online:2014-07-02 Published:2014-06-01
Contact: LIU Zhou

复杂场景中的全景密度估计方法

贺昆¹,刘舟²,戚鹿宁¹,杨恒²,朱桐¹,刘燕伟¹,周计美²

1. 公安部警卫局,北京 100031;
2. 北京正安融翰技术有限公司,北京 100088

通讯作者: 刘舟
作者简介:贺昆(1969-),男,北京人,工程师,主要研究方向:安防监控、公安保卫;刘舟(1981-),男,湖南株洲人,博士研究生,主要研究方向:模式识别、计算机视觉、图像处理;戚鹿宁(1979-),男,北京人,助理工程师,主要研究方向:安防监控、公安保卫;杨恒(1983-),男,河南商丘人,硕士研究生,主要研究方向:模式识别、计算机视觉、图像处理;朱桐(1983-),男,北京人,助理工程师,硕士研究生,主要研究方向:安防监控、公安保卫;刘燕伟(1983-),男,山东青岛人,助理工程师,硕士研究生,主要研究方向:安防监控、公安保卫;周计美(1984-),女,河北邢台人,硕士研究生,主要研究方向:模式识别、计算机视觉、图像处理。
基金资助:
公安部重点研究计划;北京市科技新星计划;北京市科技计划项目

Abstract

Abstract:

为了克服传统密度估计方法受限于算法配置工作量高、高等级密度样本数量有限等因素无法大规模应用的缺点,提出一种基于监控视频的全景密度估计方法。首先,通过自动构建场景的权重图消除成像过程中射影畸变造成的影响,该过程针对不同的场景自动鲁棒地学习出对应的权值图,从而有效降低算法配置工作量;其次,利用仿真模拟方法通过低密度等级样本构建大量高密度等级样本;最后,提取训练样本的面积、周长等特征用于训练支持向量回归机(SVR)来预测每个场景的密度等级。在测试过程中,还通过二维图像与全景地理信息系统(GIS)地图的映射,实时展示全景密度分布情况。在北京北站广场地区的深度应用结果表明,所提全景密度估计方法可以准确、快速、有效地估计复杂场景中人群密度动态变化。

摘要：

贺昆(1969-),男,北京人,工程师,主要研究方向:安防监控、公安保卫;刘舟(1981-),男,湖南株洲人,博士研究生,主要研究方向:模式识别、计算机视觉、图像处理;戚鹿宁(1979-),男,北京人,助理工程师,主要研究方向:安防监控、公安保卫;杨恒(1983-),男,河南商丘人,硕士研究生,主要研究方向:模式识别、计算机视觉、图像处理;朱桐(1983-),男,北京人,助理工程师,硕士研究生,主要研究方向:安防监控、公安保卫;刘燕伟(1983-),男,山东青岛人,助理工程师,硕士研究生,主要研究方向:安防监控、公安保卫;周计美(1984-),女,河北邢台人,硕士研究生,主要研究方向:模式识别、计算机视觉、图像处理。

CLC Number:

TP391.4

HE Kun LIU Zhou WEI Luning YANG Heng ZHU Tong LIU Yanwei ZHOU Jimei. Panoramic density estimation method in complex scene[J]. Journal of Computer Applications, 2014, 34(6): 1715-1718.

贺昆刘舟戚鹿宁杨恒朱桐刘燕伟周计美. 复杂场景中的全景密度估计方法[J]. 计算机应用, 2014, 34(6): 1715-1718.

References

[1]DAVIES A C, YIN J H, VELASTIN S A, et al. Crowd monitoring using image processing[J]. IEEE Electronics and Communication Engineering Journal, 1995, 7(1):37-47.
[2]CHO S Y, CHOW T W S, LEUNG C T. A neural-based crowd estimation by hybrid global learning algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 1999, 29(4): 535-541.
[3]BROSTOW G J, CIPOLLA R. Unsupervised Bayesian detection of independent motion in crowds[C]// CVPR 2006: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2006:594-601.
[4]RABAUD V, BELONGIE S J. Counting crowded moving objects [C]// Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2006: 705-711.
[5]KOPF M. A contribution to traffic state estimation by means of image processing methods[J]. Information Sciences and Technologies Bulletin of the ACM Slovakia, 2013, 9(5): 41-48.
[6]WU B, NEVATIA R. Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors[C]// ICCV 2005: Proceedings of the Tenth IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2005:90-97.
[7]LIN S-F, CHEN J-Y, CHAO H-X. Estimation of number of people in crowded scenes using perspective transformation[J]. IEEE Transactions on System, Man, and Cybernetics, Part A: Systems and Humans, 2001, 31(6): 645-654.
[8]ZHANG E, CHEN F. A fast and robust people counting method in video surveillance[C]// Proceedings of the 2007 International Conference on Computational Intelligence and Security. Piscataway: IEEE Press, 2007: 339-343.
[9]CHAN A B, LIANG Z-S J, VASCONCELOS N. Privacy preserving crowd monitoring: counting people without people models or tracking[C]// CVPR 2008: Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2008: 1-7.
[10]DALAL N, TRIGGS B. Histograms of oriented gradients for human detection [C]// CVPR 2005: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2005: 886-893.
[11]STAUFFER C, GRIMSON W E L. Learning patterns of activity using real-time tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757.
[12]MARANA A N, da FONTOURA COSTA L, LOTUFO R A, et al. Estimating crowd density with Minkowski fractal dimension[C]// Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Piscataway: IEEE Press, 1999,6:3521-3524.

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Panoramic density estimation method in complex scene

复杂场景中的全景密度估计方法

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