short-paper

A Detection Method of Abnormal Event in Crowds Based on Image Entropy

Authors:

Mengting LiAuthors Info & Claims

ICIIP '19: Proceedings of the 4th International Conference on Intelligent Information Processing

Pages 362 - 367

https://doi.org/10.1145/3378065.3378134

Published: 08 April 2020 Publication History

Abstract

The occurrence of group abnormal events will pose a harm to social public safety. In order to improve the detection efficiency of abnormal events in dense populations. This paper proposes an algorithm for crowd abnormal event detection based on image entropy. The algorithm firstly extracts the amplitude of the optical flow of each frame in the video by Farneback optical flow method, and then constructs the image representation of the amplitude of the optical flow. The difference of the amplitude of the optical flow of two consecutive frames will provide us with the characteristic map; A population anomaly event is identified by comparing the entropy difference between the feature maps to a particular threshold. The experimental results show that the algorithm has high detection efficiency and good real-time performance.

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Cited By

Abdul Saleem LReddy E(2023)A Survey on Deep Learning based Video Surveillance Framework2023 International Conference on Computer Communication and Informatics (ICCCI)10.1109/ICCCI56745.2023.10128302(1-6)Online publication date: 23-Jan-2023
https://doi.org/10.1109/ICCCI56745.2023.10128302
Rezaee KRezakhani SKhosravi MMoghimi M(2021)A survey on deep learning-based real-time crowd anomaly detection for secure distributed video surveillancePersonal and Ubiquitous Computing10.1007/s00779-021-01586-528:1(135-151)Online publication date: 25-Jun-2021
https://doi.org/10.1007/s00779-021-01586-5

Index Terms

A Detection Method of Abnormal Event in Crowds Based on Image Entropy
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene anomaly detection

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cover image ACM Other conferences

ICIIP '19: Proceedings of the 4th International Conference on Intelligent Information Processing

November 2019

528 pages

ISBN:9781450361910

DOI:10.1145/3378065

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Guilin: Guilin University of Technology, Guilin, China
Wuhan University of Technology: Wuhan University of Technology, Wuhan, China
International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2020

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ICIIP 2019

ICIIP 2019: 2019 4th International Conference on Intelligent Information Processing

November 16 - 17, 2019

China, China

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Overall Acceptance Rate 87 of 367 submissions, 24%

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Cited By

Abdul Saleem LReddy E(2023)A Survey on Deep Learning based Video Surveillance Framework2023 International Conference on Computer Communication and Informatics (ICCCI)10.1109/ICCCI56745.2023.10128302(1-6)Online publication date: 23-Jan-2023
https://doi.org/10.1109/ICCCI56745.2023.10128302
Rezaee KRezakhani SKhosravi MMoghimi M(2021)A survey on deep learning-based real-time crowd anomaly detection for secure distributed video surveillancePersonal and Ubiquitous Computing10.1007/s00779-021-01586-528:1(135-151)Online publication date: 25-Jun-2021
https://doi.org/10.1007/s00779-021-01586-5

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