Ma-Th算法用于密集人群中的人计数及其行为分类

B. Yogameena, N. Packiyaraj, S. S. Perumal, P. Saravanan
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引用次数: 7

摘要

本文提出了一种智能监控算法,用于估计人群中的人数,并对人群行为进行正常或异常的分类。该方法结合机器学习和基于阈值的算法(Ma-Th)来估计人数和人群行为分析。首先,利用ViBe算法对前景进行分割。随后,使用边界盒特征(如人群密度、相对高度/宽度、前景像素的水平/垂直平均值)提取特征。此外,对前景像素的动能和人群分布进行阈值化。这些特征是通过相关向量机(RVM)学习算法来学习的,用于人数统计和行为分类。利用Pets 2009、UMN、UCSD等基准监测数据集和从网络下载的视频进行了实验,结果表明了算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ma-Th algorithm for people count in a dense crowd and their behaviour classification
In this paper, an intelligent surveillance algorithm for estimating the people count in a crowd and also classifying the crowd behavior as normal or abnormal is proposed. This method combines the machine learning and threshold based algorithms (Ma-Th) to estimate the people count and crowd behavior analysis. First, the foreground is segmented using ViBe algorithm. Subsequently, the features are extracted using bounding box characteristics such as crowd density, relative height/width, foreground pixel's horizontal/vertical mean. In addition to that the foreground pixel's kinetic energy and crowd distribution are thresholded. These features are learnt by Relevance Vector Machine (RVM) learning algorithm for both people count and their behavior classification. Experimental results obtained by using benchmark surveillance datasets such as Pets 2009, UMN, UCSD and videos downloaded from internet show the effectiveness of the proposed algorithm.
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