基于复合特征及深度学习的人群行为识别算法

doi:10.11896/j.issn.1002-137X.2019.06.046

摘要/Abstract

摘要： 分析人群行为的目的是更好地分析与管理人群运动的状态与趋势。针对人群行为的两种特征信息,提出了一种基于深度学习的人群行为识别方法。先将人群作为主要对象,通过前景提取方法来提取人群静态信息,利用人群运动的变化获取人群动态信息,借助卷积神经网络(CNN)模型学习这两种不同的人群行为特征,再综合这两种特征来分析常见的人群行为。同时,人群数据提取位置与间隔是影响人群行为分析的重要因素。实验结果表明,这两种人群特征能更好地描述空间维度上的人群状态和时间维度上的人群变化,合理的数据位置与数据间隔可以有效地提高人群信息的表达能力。最后将提出的方法与其他人群行为分析方法进行比较,定量与定性的实验结果验证了所提方法的有效性,同时也表明了所提方法能得到更优的混淆矩阵和更高的准确度。

关键词: CNN, 动态特征, 静态特征, 人群行为识别, 数据提取

Abstract: The target of analyzing crowd behavior is to better analyze and manage the state and tendency of crowd movement.This paper proposed a novel deep learning based crowd behavior recognition method by using two types of crowd behavior features.Firstly,the crowd is regarded as the main object,a foreground extraction method is used to extract the static information of crowd,and the dynamic information of crowd is obtained by the change of the crowd movement.Then two different crowd behavior characteristics are learned by using convolution neural network (CNN) model,so as to analyze crowd behaviors in the end.Additionally,the extraction location and interval of crowd data are crucial factors in the crowd behavior recognition.Experimental results show that two crowd characteristics can better describe crowd states on the spatial dimension and crowd changes on the temporal dimension.The rational data location and data interval can effectively improve the expression ability of crowd information.At last,this method was compared with other crowd behavior recognition algorithms.The quantitative and qualitative experimental results demonstrate the validity of the proposed method.Besides,better confusion matrix and higher precision can be obtained by this method.

Key words: CNN, Crowd behavior recognition, Data extraction, Dynamic characteristic, Static characteristic

中图分类号:

TP391

袁亚军, 李菲菲, 陈虬. 基于复合特征及深度学习的人群行为识别算法[J]. 计算机科学, 2019, 46(6): 305-310. https://doi.org/10.11896/j.issn.1002-137X.2019.06.046

YUAN Ya-jun, LEE Fei-fei, CHEN Qiu. Crowd Behavior Recognition Algorithm Based on Combined Features and Deep Learning[J]. Computer Science, 2019, 46(6): 305-310. https://doi.org/10.11896/j.issn.1002-137X.2019.06.046

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