标题:Violent crowd behavior detection using deep learning and compressive sensing
作者:Gao, Mingliang ;Jiang, Jun ;Ma, Lixiu ;Zhou, Shuwen ;Zou, Guofeng ;Pan, Jinfeng ;Liu, Zheng
通讯作者:Jiang, Jun
作者机构:[Gao, Mingliang ;Ma, Lixiu ;Zou, Guofeng ;Pan, Jinfeng ] School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo; 255 更多
会议名称:31st Chinese Control and Decision Conference, CCDC 2019
会议日期:3 June 2019 through 5 June 2019
来源:Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019
出版年:2019
页码:5329-5333
DOI:10.1109/CCDC.2019.8832598
关键词:compressive sensing; compressive sensing (CS) has; deep learning generalization. Recently; violent crowd behavior
摘要:In this paper, we propose a new crowd analysis method fusing deep learning network and compressive sensing for violent behavior detection. To this aim, a novel hybrid random matrix (HRM) is constructed and is proved to satisfy the restricted isometry property. The high-dimensional features can be projected to a low-dimensional space via the HRM. Furthermore, a deep neural network is developed for extracting crowd behaviour representations based on reduced dimension features. Finally, the learnt deep features are used for classification. Experimental results demonstrate that the proposed method is effective and efficient in violent behavior detection, and is on par with or better than the state-of-the-art methods. © 2019 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073096434&doi=10.1109%2fCCDC.2019.8832598&partnerID=40&md5=d67730b1154f18eb1fa269ceca694a30
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