research-article

Anomaly Detection in Crowd Scene via Online Learning

Authors:

Yuan YuanAuthors Info & Claims

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

Pages 158 - 162

https://doi.org/10.1145/2632856.2632862

Published: 10 July 2014 Publication History

Abstract

Anomaly detection in crowd scene has attracted an increasing attention in video surveillance, but a precise detection still remains a challenge. This paper presents a novel online learning method to automatically detect abnormal behaviors in crowd scene. Our focus is mainly on the deviation between the real motion and the predicted one. Through online defining experts, analyzing their motions, and dynamically updating the learned model, anomaly can be identified by the final expert joint decision. The outputs are represented as the anomaly probability of an examined frame. Compared with most of existing methods, the proposed one needs neither tracking each individual straight to the end nor requires any complex training procedure. We test the proposed method on public datasets, and the results show its effectiveness.

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Cited By

de Paula DSalvadeo Dde Araujo D(2022)CamNuvem: A Robbery Dataset for Video Anomaly DetectionSensors10.3390/s22241001622:24(10016)Online publication date: 19-Dec-2022
https://doi.org/10.3390/s222410016
Hao YXu ZLiu YWang JFan J(2019)Effective Crowd Anomaly Detection Through Spatio-temporal Texture AnalysisInternational Journal of Automation and Computing10.1007/s11633-018-1141-z16:1(27-39)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s11633-018-1141-z
Ramchandran ASangaiah A(2019)Unsupervised deep learning system for local anomaly event detection in crowded scenesMultimedia Tools and Applications10.1007/s11042-019-7702-5Online publication date: 12-May-2019
https://doi.org/10.1007/s11042-019-7702-5
Show More Cited By

Index Terms

Anomaly Detection in Crowd Scene via Online Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Video summarization

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Published In

cover image ACM Other conferences

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

July 2014

430 pages

ISBN:9781450328104

DOI:10.1145/2632856

General Chairs:
Hanzi Wang
Xiamen University, China
,
Larry Davis
University of Maryland, USA
,
Program Chairs:
Wenwu Zhu
Tsinghua University, China
,
Stephan Kopf
University of Mannheim, Germany
,
Yanyun Qu
Xiamen University, China
,
Publications Chairs:
Jun Yu
Xiamen University, China
,
Jitao Sang
Institute of Automation, CAS, China
,
Tao Mei
Microsoft Research Asia, China

Copyright © 2014 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

NSF of China: National Natural Science Foundation of China
Beijing ACM SIGMM Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2014

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ICIMCS '14

ICIMCS '14: International Conference on Internet Multimedia Computing and Service

July 10 - 12, 2014

Xiamen, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

de Paula DSalvadeo Dde Araujo D(2022)CamNuvem: A Robbery Dataset for Video Anomaly DetectionSensors10.3390/s22241001622:24(10016)Online publication date: 19-Dec-2022
https://doi.org/10.3390/s222410016
Hao YXu ZLiu YWang JFan J(2019)Effective Crowd Anomaly Detection Through Spatio-temporal Texture AnalysisInternational Journal of Automation and Computing10.1007/s11633-018-1141-z16:1(27-39)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s11633-018-1141-z
Ramchandran ASangaiah A(2019)Unsupervised deep learning system for local anomaly event detection in crowded scenesMultimedia Tools and Applications10.1007/s11042-019-7702-5Online publication date: 12-May-2019
https://doi.org/10.1007/s11042-019-7702-5
Chen MWang QLi X(2017)Anchor-based group detection in crowd scenes2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2017.7952382(1378-1382)Online publication date: Mar-2017
https://doi.org/10.1109/ICASSP.2017.7952382

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