article

Video crowd detection and abnormal behavior model detection based on machine learning method

Authors:

Xiaohong Zhang,

Jing CaiAuthors Info & Claims

Neural Computing and Applications, Volume 31, Issue 1

Pages 175 - 184

https://doi.org/10.1007/s00521-018-3692-x

Published: 01 January 2019 Publication History

Abstract

Pedestrian detection and abnormal behavior detection is the computer for a given image and video, to determine whether there are pedestrians and their behavior is normal. Pedestrian detection is the basis and premise of pedestrian tracking, behavior analysis, gait analysis, pedestrian identity recognition and so on. A good pedestrian detection algorithm can provide strong support and guarantee for the latter. The overall goal of this project is to learn different data mining methods and try to improve the detection accuracy of video crowd machine abnormal behavior. Aiming at the shortage of user behavior anomaly detection model proposed by Lane et al., a new IDS anomaly detection model is proposed. This model improves the representation of user behavior patterns and behavior profiles and adopts a new similarity assignment method. Experiments based on Unix user shell command data show that the detection model proposed in this paper has higher detection performance.

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Cited By

Rezaee KRezakhani SKhosravi MMoghimi M(2024)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: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s00779-021-01586-5
Wang ZLi ZLeng JLi MBai L(2023)Multiple Pedestrian Tracking With Graph Attention Map on Urban Road SceneIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.319396124:8(8567-8579)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TITS.2022.3193961
Hussain TKhan SUllah WHaq IKim MLee MBaik S(2023)eCubeLand: An Intelligent Multiview Video Data ModelingIEEE MultiMedia10.1109/MMUL.2023.328995330:4(5-15)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/MMUL.2023.3289953
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Index Terms

Video crowd detection and abnormal behavior model detection based on machine learning method
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 31, Issue 1

January 2019

978 pages

ISSN:0941-0643

EISSN:1433-3058

Issue’s Table of Contents

Copyright © Copyright © 2019 Springer-Verlag London Ltd., part of Springer Nature.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2019

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Citations

Cited By

Rezaee KRezakhani SKhosravi MMoghimi M(2024)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: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s00779-021-01586-5
Wang ZLi ZLeng JLi MBai L(2023)Multiple Pedestrian Tracking With Graph Attention Map on Urban Road SceneIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.319396124:8(8567-8579)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TITS.2022.3193961
Hussain TKhan SUllah WHaq IKim MLee MBaik S(2023)eCubeLand: An Intelligent Multiview Video Data ModelingIEEE MultiMedia10.1109/MMUL.2023.328995330:4(5-15)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/MMUL.2023.3289953
Singh JKumar M(2023)Chronological ant lion optimizer-based deep convolutional neural network for panic behavior detection in crowded scenesMultimedia Tools and Applications10.1007/s11042-023-14598-582:21(32373-32396)Online publication date: 6-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-023-14598-5
Fu JZhou XMei G(2022)Internet Digital Economy Development Forecast Based on Artificial Intelligence and SVM-KNN Network DetectionComputational Intelligence and Neuroscience10.1155/2022/57926942022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5792694
Yuanfei Z(2022)A Personalized Recommendation System for English Teaching Resources Based on Learning Behavior DetectionMobile Information Systems10.1155/2022/45318672022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4531867
Zhang NRen JXu QWu HWang M(2022)Anomaly Recognition Algorithm for Human Multipose Motion Behavior Using Generative Adversarial NetworkWireless Communications & Mobile Computing10.1155/2022/26560012022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2656001
Chacon-Murguia MGuzman-Pando A(2022)Moving Object Detection in Video Sequences Based on a Two-Frame Temporal Information CNNNeural Processing Letters10.1007/s11063-022-11092-155:5(5425-5449)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1007/s11063-022-11092-1
Raja RSharma PMahmood MSaini D(2022)Analysis of anomaly detection in surveillance video: recent trends and future visionMultimedia Tools and Applications10.1007/s11042-022-13954-182:8(12635-12651)Online publication date: 27-Sep-2022
https://dl.acm.org/doi/10.1007/s11042-022-13954-1
Zhang CRen K(2022)LRATD: a lightweight real-time abnormal trajectory detection approach for road traffic surveillanceNeural Computing and Applications10.1007/s00521-022-07626-234:24(22417-22434)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s00521-022-07626-2
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