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research-article

Abnormal behavior detection using hybrid agents in crowded scenes

Authors:

Hang-Bong KangAuthors Info & Claims

Pattern Recognition Letters, Volume 44, Issue C

Pages 64 - 70

https://doi.org/10.1016/j.patrec.2013.11.017

Published: 15 July 2014 Publication History

Abstract

We categorize the behaviors of people into individual and group interactive behavior.We propose a hybrid agent system that includes static and dynamic agents in a scene.We represent the behavior of a crowd as a bag of words to detect abnormal behavior. In this paper, we propose a hybrid agent method to detect abnormal behaviors in a crowded scene. In real-life situations, abnormal behavior occurs by violent movement which is apparent as sudden speeding up, or chaotic movement in a restricted area, or movement contrasting with that of one's neighbors such as in a panic situation. In our model, we categorize the behaviors of people into individual behavior and group interactive behavior. Individual behavior is defined only by native motion information such as speed and direction. By contrast, group interactive behavior is defined by information concerning interactive motion between neighbors. We propose a hybrid agent system that includes static and dynamic agents to observe efficiently the corresponding individual and interactive behaviors in a crowded scene. The static agent is assigned to a specific spot and analyzes motion information near that spot. Unlike the static agent, the dynamic agent is assigned to a moving object and analyzes motion information of neighbors as well as oneself by following the object's movement. We represent the behavior of a crowd as a bag of words through the integration of static and dynamic agent information to determine abnormalities in the crowd behavior. The experimental results show that our proposed method efficiently detects abnormal behaviors in crowded scenes.

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Cited By

Santos CClaro DMedrado Gondim JMane B(2024)Suspicious Behavior Detection near Vehicles in University Environment: An Approach using Object Detection and Body AnglesProceedings of the 20th Brazilian Symposium on Information Systems10.1145/3658271.3658338(1-10)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3658271.3658338
Ullah FObaidat MUllah AMuhammad KHijji MBaik S(2023)A Comprehensive Review on Vision-Based Violence Detection in Surveillance VideosACM Computing Surveys10.1145/356197155:10(1-44)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3561971
Zhu Y(2022)A Deep Learning-Based Model for Analyzing Social Public IssuesSecurity and Communication Networks10.1155/2022/73996002022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7399600
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Abnormal behavior detection using hybrid agents in crowded scenes
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Computer graphics
    1. Animation

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Information & Contributors

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Published In

cover image Pattern Recognition Letters

Pattern Recognition Letters Volume 44, Issue C

July 2014

171 pages

ISSN:0167-8655

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 15 July 2014

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Cited By

Santos CClaro DMedrado Gondim JMane B(2024)Suspicious Behavior Detection near Vehicles in University Environment: An Approach using Object Detection and Body AnglesProceedings of the 20th Brazilian Symposium on Information Systems10.1145/3658271.3658338(1-10)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3658271.3658338
Ullah FObaidat MUllah AMuhammad KHijji MBaik S(2023)A Comprehensive Review on Vision-Based Violence Detection in Surveillance VideosACM Computing Surveys10.1145/356197155:10(1-44)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3561971
Zhu Y(2022)A Deep Learning-Based Model for Analyzing Social Public IssuesSecurity and Communication Networks10.1155/2022/73996002022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7399600
Huang JLi JWu W(2022)A New Method of Pedestrian Abnormal Behavior Detection Based on Attention GuidanceAdvances in Multimedia10.1155/2022/10382252022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1038225
Tripathi GSingh KVishwakarma D(2019)Convolutional neural networks for crowd behaviour analysisThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-018-1499-535:5(753-776)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s00371-018-1499-5
Zhou WWang LHuang YQing LWu XHe X(2019)Facial Expression Recognition Based on Group Domain Random Frame ExtractionImage and Graphics10.1007/978-3-030-34120-6_38(467-479)Online publication date: 23-Aug-2019
https://dl.acm.org/doi/10.1007/978-3-030-34120-6_38
Ben Mabrouk AZagrouba E(2018)Abnormal behavior recognition for intelligent video surveillance systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.09.02991:C(480-491)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.eswa.2017.09.029
Zheng AZhao YLi CTang JLuo B(2018)Multispectral Foreground Detection via Robust Cross-Modal Low-Rank DecompositionAdvances in Multimedia Information Processing – PCM 201810.1007/978-3-030-00776-8_75(819-829)Online publication date: 21-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-00776-8_75
Fradi HLuvison BPham Q(2017)Crowd Behavior Analysis Using Local Mid-Level Visual DescriptorsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2016.261544327:3(589-602)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1109/TCSVT.2016.2615443
Zitouni MBhaskar HDias JAl-Mualla M(2016)Advances and trends in visual crowd analysisNeurocomputing10.1016/j.neucom.2015.12.070186:C(139-159)Online publication date: 19-Apr-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.12.070
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