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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 3
891-895

Abnormal crowd behavior detection and localization based on deep spatial-temporal convolutional neural network

Hu Xuemin
Chen Qin
Yang Li
Yu Jin
Tong Xiuchi
School of Computer Science & Information Engineering, Hubei University, Wuhan 430062, China
DOI: 10.19734/j.issn.1001-3695.2018.09.0671

Abstract

To handle the issues of low accuracy and lacking training samples in abnormal crowd behavior detection in public places, this paper proposed a method based on deep spatial-temporal convolutional neural networks. In view of the characteristics of crowd behavior in monitoring videos, it first designed a deep spatial-temporal convolution neural network for detecting abnormal crowd behavior by extending 2D convolution to the 3D space according to spatial features of static images and temporal features between the frames before and after the current frame. To locating abnormal crowd behaviors, this paper divided video frames into a number of sub-regions and obtained spatial-temporal samples of sub-regions. Then, it input the samples into the designed deep spatial-temporal convolutional neural network for training and classification, so as to detect and locate abnormal crowd behaviors. Meanwhile, to deal with the issue of lacking training samples when training the deep spatial-temporal convolutional neural network, a transfer learning method was designed to use datasets with more training samples to pre-train the network. Then it fine-tuned and optimized the network model in the datasets to be tested. Experimental results show that the detection accuracies on UCSD and subway open datasets are greater than 99% and 93% respectively.

Foundation Support

国家自然科学基金青年基金资助项目(61806076)
湖北省自然科学基金青年资助项目(2018CFB158)
湖北省大学生创新创业训练计划基金资助项目(201710512049)
湖北省人文社科重点研究基地开放课题(2015JX04)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.09.0671
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 3
Section: Technology of Graphic & Image
Pages: 891-895
Serial Number: 1001-3695(2020)03-056-0891-05

Publish History

[2020-03-05] Printed Article

Cite This Article

胡学敏, 陈钦, 杨丽, 等. 基于深度时空卷积神经网络的人群异常行为检测和定位 [J]. 计算机应用研究, 2020, 37 (3): 891-895. (Hu Xuemin, Chen Qin, Yang Li, et al. Abnormal crowd behavior detection and localization based on deep spatial-temporal convolutional neural network [J]. Application Research of Computers, 2020, 37 (3): 891-895. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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