research-article

Abnormal crowd behavior detection and localization using maximum sub-sequence search

Authors:

Yie-Tarng Chen,

Wen-Hsien FangAuthors Info & Claims

ARTEMIS '13: Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream

Pages 49 - 58

https://doi.org/10.1145/2510650.2510655

Published: 21 October 2013 Publication History

Abstract

This paper presents a novel framework for anomaly event detection and localization in crowded scenes. We propose an anomaly detector that extends the Bayes classifier from multi-class to one-class classification to characterize normal events. We also propose a localization scheme for anomaly localization as a maximum subsequence problem in a video sequence. The maximum subsequence algorithm locates an anomaly event by discovering the optimal collection of successive patches with spatial proximity over time without prior knowledge of the size, start and end of the anomaly event. Our localization scheme can locate multiple occurrences of abnormal events in spite of noise. Experimental results on the well-established UCSD dataset show that the proposed framework significantly outperforms state-of-the-art methods up to 53.55% localization rate. This study concludes that the localization framework plays an important role in abnormal event detection.

References

[1]

R. Mehran, A. Oyama, and M. Shah, "Abnormal crowd behavior detection using social force model," in CVPR, Jun. 2009, pp. 935--942.

[2]

V. Mahadevan, V. B. W. Li, and N. Vasconcelos, "Anomaly detection in crowded scenes," in CVPR, Jun. 2010, pp. 1975--1981.

[3]

Y. Cong, J. Yuan, and J. Liu, "Sparse reconstruction cost for abnormal event detection," in CVPR, Jun. 2011, pp. 3449--3456.

Digital Library

[4]

A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz, "Robust real-time unusual event detection using multiple fixed-location monitors," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, pp. 555--560, Jan. 2008.

Digital Library

[5]

Y. Benezeth, P. Jodoin, V. Saligrama, and C. Rosenberger, "Abnormal events detection based on spatio-temporal co-occurences," in CVPR, Jun. 2009, pp. 2458 -- 2465.

[6]

V. Saligrama and Z. Chen, "Video anomaly detection based on local statistical aggregates," in CVPR, Jun. 2012, pp. 2112 -- 2119.

Digital Library

[7]

D. Helbing and P. MolnaI Ar, "Social force model for pedestrian dynamics," Physical Review E, vol. 51, pp. 4282--4286, May 1995.

[8]

A. Chan and N. Vasconcelos, "Modeling, clustering, and segmenting video with mixtures of dynamic textures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, pp. 909 -- 926, May 2008.

Digital Library

[9]

C. H. Lampert, M. B. Blaschko, and T. Hofmann, "Beyond sliding windows: Object localization by efficient subwindow search," in CVPR, Jun. 2008, pp. 1--8.

[10]

J. Yuan, Z. Liu, and Y. Wu, "Discriminative subvolume search for efficient action detection," in CVPR, Jun. 2009, pp. 2442 -- 2449.

[11]

D. Tran and J. Yuan, "Optimal spatio-temporal path discovery for video event detection," in CVPR, Jun. 2011, pp. 3321 -- 3328.

Digital Library

[12]

B. Scholkopt, J. Platt, J. Shawe-Taylor, A. Smola, and R. Williamson, "Estimating the support of a high-dimensional distribution," Neural Computation, vol. 13, pp. 1443--1471, Jul. 2001.

Digital Library

[13]

D. G. Lowe, "Distinctive image features from scale-invariant keypoints," IJCV, vol. 60, pp. 91--110, Nov. 2004.

Digital Library

[14]

N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in CVPR, Jun. 2005, pp. 886--893.

Digital Library

[15]

N. Dalal, B. Triggs, and C. Schmid, "Human detection using oriented histograms of flow and appearance," in ECCV, May 2006, pp. 428--441.

Digital Library

Cited By

Lin WGao JWang QLi X(2021)Learning to detect anomaly events in crowd scenes from synthetic dataNeurocomputing10.1016/j.neucom.2021.01.031436(248-259)Online publication date: May-2021
https://doi.org/10.1016/j.neucom.2021.01.031
Venkataramanan SPeng KSingh RMahalanobis A(2020)Attention Guided Anomaly Localization in ImagesComputer Vision – ECCV 202010.1007/978-3-030-58520-4_29(485-503)Online publication date: 19-Nov-2020
https://doi.org/10.1007/978-3-030-58520-4_29
Aziz MNaeem FAlizai MKhan K(2018)Automated Solutions for Crowd Size EstimationSocial Science Computer Review10.1177/089443931772651036:5(610-631)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1177/0894439317726510
Show More Cited By

Index Terms

Abnormal crowd behavior detection and localization using maximum sub-sequence search
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Video summarization
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Paths and connectivity problems

Recommendations

An efficient subsequence search for video anomaly detection and localization

This paper presents a novel framework for anomaly event detection and localization in crowded scenes. For anomaly detection, one-class support vector machine with Bayesian derivation is applied to detect unusual events. We also propose a novel event ...
Video crowd detection and abnormal behavior model detection based on machine learning method

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 ...
CViT: A Convolution Vision Transformer for Video Abnormal Behavior Detection and Localization
Abstract
Video anomaly detection is a critical task because of the rare, irregular, and unbounded nature of abnormal events. Currently, most approaches only rely on CNN for such tasks, but due to spatial inductive bias, it can extract only local features ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ARTEMIS '13: Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream

October 2013

94 pages

ISBN:9781450323932

DOI:10.1145/2510650

General Chairs:
Anastasios Doulamis
Technical University of Crete/National Technical University of Athens, Greece
,
Marco Bertini
University of Florence, Italy
,
Nikolaos Doulamis
National Technical University of Athens, Greece
,
Jordi Gonzàlez
Universitat Autònoma de Barcelona, Spain
,
Program Chair:
Athanasios Voulodimos
National Technical University of Athens, Greece

Copyright © 2013 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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MM '13

Sponsor:

SIGMM

MM '13: ACM Multimedia Conference

October 21, 2013

Barcelona, Spain

Upcoming Conference

MM '24

Sponsor:
sigmm

The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

Melbourne , VIC , Australia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
288
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 22 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lin WGao JWang QLi X(2021)Learning to detect anomaly events in crowd scenes from synthetic dataNeurocomputing10.1016/j.neucom.2021.01.031436(248-259)Online publication date: May-2021
https://doi.org/10.1016/j.neucom.2021.01.031
Venkataramanan SPeng KSingh RMahalanobis A(2020)Attention Guided Anomaly Localization in ImagesComputer Vision – ECCV 202010.1007/978-3-030-58520-4_29(485-503)Online publication date: 19-Nov-2020
https://doi.org/10.1007/978-3-030-58520-4_29
Aziz MNaeem FAlizai MKhan K(2018)Automated Solutions for Crowd Size EstimationSocial Science Computer Review10.1177/089443931772651036:5(610-631)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1177/0894439317726510
Isupova OKuzin DMihaylova L(2018)Learning Methods for Dynamic Topic Modeling in Automated Behavior AnalysisIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2017.273536429:9(3980-3993)Online publication date: Sep-2018
https://doi.org/10.1109/TNNLS.2017.2735364
Xie WYin HWang MShao YYu B(2018)Low‐rank structured sparse representation and reduced dictionary learning‐based abnormity detectionIET Computer Vision10.1049/iet-cvi.2018.525613:1(8-14)Online publication date: 4-Dec-2018
https://dl.acm.org/doi/10.1049/iet-cvi.2018.5256
Isupova OIsupova O(2018)BackgroundMachine Learning Methods for Behaviour Analysis and Anomaly Detection in Video10.1007/978-3-319-75508-3_2(9-35)Online publication date: 25-Feb-2018
https://doi.org/10.1007/978-3-319-75508-3_2
Yu BLiu YSun Q(2017)A Content-Adaptively Sparse Reconstruction Method for Abnormal Events Detection With Low-Rank PropertyIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2016.263804847:4(704-716)Online publication date: Apr-2017
https://doi.org/10.1109/TSMC.2016.2638048
Yu BLiu YSun Q(2016)Low-rank approximation based abnormal detection in the video sequence2016 IEEE International Conference on Digital Signal Processing (DSP)10.1109/ICDSP.2016.7868530(129-133)Online publication date: Oct-2016
https://doi.org/10.1109/ICDSP.2016.7868530
Burney ASyed T(2016)Crowd Video Classification Using Convolutional Neural Networks2016 International Conference on Frontiers of Information Technology (FIT)10.1109/FIT.2016.052(247-251)Online publication date: Dec-2016
https://doi.org/10.1109/FIT.2016.052
Wang GFu HLiu Y(2016)Real time abnormal crowd behavior detection based on adjacent flow location estimation2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS)10.1109/CCIS.2016.7790305(476-479)Online publication date: Aug-2016
https://doi.org/10.1109/CCIS.2016.7790305
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents