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首页> 外文期刊>International Journal of Information Acquisition >MULTI-RESOLUTION CROWD DENSITY ESTIMATION BASED ON TEXTURE ANALYSIS AND LEARNING FROM DEMONSTRATION
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MULTI-RESOLUTION CROWD DENSITY ESTIMATION BASED ON TEXTURE ANALYSIS AND LEARNING FROM DEMONSTRATION

机译：基于纹理分析和演示学习的多分辨率人群密度估计

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摘要

Crowd density estimation is very important for intelligent surveillance systems in public places. This paper presents an automatic method of estimating crowd density using texture analysis and machine learning. First the crowd scene is modeled as a series of multi-resolution image cells based on perspective projection. The cell size is normalized to obtain a uniform representation of texture features. Then the feature vectors of textures are extracted from each input image cell and the support vector machine (SVM) method is utilized to solve the regression problem for calculating the crowd density. In order to diminish the instability of texture feature measurements, a technique of searching the extrema in the Harris-Laplacian space is applied. Finally, the SVM method is used again to detect some abnormal situations caused by the changes in density distribution. Experiments on real crowd videos show the effectiveness of the proposed system.
机译：人群密度估计对于公共场所的智能监控系统非常重要。本文提出了一种使用纹理分析和机器学习估计人群密度的自动方法。首先，将人群场景建模为基于透视投影的一系列多分辨率图像单元。归一化单元大小以获得纹理特征的统一表示。然后从每个输入图像单元中提取纹理的特征向量，并利用支持向量机（SVM）方法解决计算人群密度的回归问题。为了减少纹理特征测量的不稳定性，应用了一种在哈里斯-拉普拉斯空间中搜索极值的技术。最后，再次使用SVM方法来检测由密度分布的变化引起的一些异常情况。在真实人群视频上的实验证明了该系统的有效性。

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