article

Visual data mining for crowd anomaly detection using artificial bacteria colony

Authors:

Luiza Macedo Mourelle,

Brij B. GuptaAuthors Info & Claims

Multimedia Tools and Applications, Volume 77, Issue 14

Pages 17755 - 17777

https://doi.org/10.1007/s11042-017-5382-6

Published: 01 July 2018 Publication History

Abstract

This paper presents a novel method for global anomaly detection in crowded scenes. The optical flow of frames is used to extract the foreground of areas with people motions in the crowd in the form of layers. The optical flow between two frames generates one layer. The proposed method applies the metaheuristic of artificial bacteria colony as a robust algorithm to optimize the extracted layers. Artificial bacteria cover all regions of interest that have high movement between frames. The artificial bacteria colony adapts quickly to the most varied scenarios. Moreover, the algorithm has low sensibility to noise and to sudden changes in video lighting as captured by optical flow. The bacteria population of the colonies, its food storage and the colony's centroid position regarding each optical flow layer, are used as input to train a Kohonen's neural network. Once trained the network is able to detect specific events based on behavior patterns similarity, as produced by the bacteria colony during such events. Experiments are conducted on available public dataset. The achieved results show that the proposed method captures the dynamics of the crowd behavior successfully, revealing that the proposed scheme outperforms the available state-of-the-art algorithms for global anomaly detection.

References

[1]

Andrade EL, Blunsden S, Fisher RB (2006) Modelling crowd scenes for event detection. In: 18th International Conference on Pattern Recognition (ICPR'06), vol 1. IEEE, pp 175---178

Digital Library

[2]

Brostow GJ, Cipolla R (2006) Unsupervised bayesian detection of independent motion in crowds. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), vol 1, pp 594---601

Digital Library

[3]

Chaaraoui AA, Climent-Pérez P, Flórez-Revuelta F (2012) A review on vision techniques applied to human behaviour analysis for ambient-assisted living. Expert Syst Appl 39(12):10873---10888

Digital Library

[4]

Cong Y, Yuan J, Liu J (2011) Sparse reconstruction cost for abnormal event detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 3449---3456

Digital Library

[5]

Dataset UMN (2006) Unusual crowd activity dataset of university of minnesota, available in http://mha.cs.umn.edu/movies/crowdactivity-all.avi. Minessota

[6]

Feng J, Zhang C, Hao P (2010) Online learning with self-organizing maps for anomaly detection in crowd scenes. In: 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, pp 3599---3602

Digital Library

[7]

Garate C, Bilinsky P, Bremond F (2009) Crowd event recognition using hog tracker. In: 2009 Twelfth IEEE international workshop on performance evaluation of tracking and surveillance (PETS-Winter), pp 1---6

[8]

Horn BKP, Schunck BG (1981) Determining optical flow. Artificial intelligence 17(1---3):185---203

Digital Library

[9]

Kajo I, Malik AS, Kamel N (2015) Motion estimation of crowd flow using optical flow techniques: a review. In: 2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS). IEEE, pp 1---9

[10]

Kaltsa Vagia, Briassouli Alexia, Kompatsiaris Ioannis, Hadjileontiadis Leontios J, Strintzis Michael Gerasimos (2015) Swarm intelligence for detecting interesting events in crowded environments. IEEE Trans Image Process 24(7):2153---2166

[11]

Kaltsa V, Briassouli A, Kompatsiaris I, Strintzis MG (2014) Swarm-based motion features for anomaly detection in crowds. In: 2014 IEEE international conference on image processing (ICIP). IEEE, Paris, pp 2353---2357

[12]

Lui YM (2012) Tangent bundles on special manifolds for action recognition. IEEE Transactions on Circuits and Systems for Video Technology 22(6):930---942

Digital Library

[13]

Mahadevan V, Li W, Bhalodia V, Vasconcelos N (2010) Anomaly detection in crowded scenes. In: CVPR, vol 249, p 250

[14]

Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, p 2009

[15]

Murray JD (2002) Mathematical biology i: an introduction, vol 17 of interdisciplinary applied mathematics

[16]

Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 22(3):52---67

[17]

Popoola OP, Wang K (2012) Video based abnormal human behavior recognition a review. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):865---878

Digital Library

[18]

Raghavendra R, Del Bue A, Cristani M, Murino V (2011) Optimizing interaction force for global anomaly detection in crowded scenes. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), IEEE, pp 136---143

[19]

Rao AS, Gubbi J, Marusic S, Stanley P, Palaniswami M (2013) Crowd density estimation based on optical flow and hierarchical clustering. In: 2013 international conference on advances in computing, communications and informatics (ICACCI). IEEE, p 2013

[20]

Thida M, Eng H-L, Remagnino P (2013) Laplacian eigenmap with temporal constraints for local abnormality detection in crowded scenes. IEEE Trans Cybern 43 (6):2147---2156

[21]

Shandong W, Moore BE, Shah M (2010) Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, p 2010

[22]

Xinxiao W, Dong X, Duan L, Luo J, Jia Y (2013) Action recognition using multilevel features and latent structural svm. IEEE Transactions on Circuits and Systems for Video Technology 23(8):1422---1431

Digital Library

Cited By

Alhothali ABalabid AAlharthi RAlzahrani BAlotaibi RBarnawi A(2023)Anomalous event detection and localization in dense crowd scenesMultimedia Tools and Applications10.1007/s11042-022-13967-w82:10(15673-15694)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s11042-022-13967-w
Liu ZSakulyeva TMikheev AStepanova D(2021)Management Problems in Global CrowdsourcingJournal of Global Information Management10.4018/JGIM.20220701.oa330:3(1-15)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.4018/JGIM.20220701.oa3
Wang JRao YShi XChen X(2021)Picture Preview Generation for Interactive Educational ResourcesComplexity10.1155/2021/55368672021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/5536867
Show More Cited By

Index Terms

Visual data mining for crowd anomaly detection using artificial bacteria colony
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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

Recommendations

Abnormal Crowd Behavior Detection Using Topological Methods
SNPD '11: Proceedings of the 2011 12th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

In this paper we present a novel framework for abnormal behavior detection in crowded scenes. For this purpose, the theory of topological simplification on the dense field is extended to the sparse particle motion field, which is used to describe the ...
Global Artificial Bee Colony-Levenberq-Marquardt GABC-LM Algorithm for Classification

The performance of Neural Networks NN depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect's behavior learning ...
Functional Link Neural Network with Modified Artificial Bee Colony for Data Classification

Functional Link Neural Network FLNN has emerged as an important tool for solving non-linear classification problem and has been successfully applied in many engineering and scientific problems. The FLNN structure is much more modest than ordinary feed ...

Comments

Information & Contributors

Information

Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 77, Issue 14

Jul 2018

1326 pages

ISSN:1380-7501

Issue’s Table of Contents

Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2018

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
0
Total Downloads

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

Reflects downloads up to 11 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Alhothali ABalabid AAlharthi RAlzahrani BAlotaibi RBarnawi A(2023)Anomalous event detection and localization in dense crowd scenesMultimedia Tools and Applications10.1007/s11042-022-13967-w82:10(15673-15694)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s11042-022-13967-w
Liu ZSakulyeva TMikheev AStepanova D(2021)Management Problems in Global CrowdsourcingJournal of Global Information Management10.4018/JGIM.20220701.oa330:3(1-15)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.4018/JGIM.20220701.oa3
Wang JRao YShi XChen X(2021)Picture Preview Generation for Interactive Educational ResourcesComplexity10.1155/2021/55368672021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/5536867
Nedjah NRibeiro Lde Macedo Mourelle L(2021)Communication optimization for efficient dynamic task allocation in swarm roboticsApplied Soft Computing10.1016/j.asoc.2021.107297105:COnline publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1016/j.asoc.2021.107297
Sohrabi MHemmatian F(2019)An efficient preprocessing method for supervised sentiment analysis by converting sentences to numerical vectors: a twitter case studyMultimedia Tools and Applications10.1007/s11042-019-7586-478:17(24863-24882)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s11042-019-7586-4
(2018)IP assignment for efficient NoC-based system design using multi-objective particle swarm optimisationInternational Journal of Bio-Inspired Computation10.5555/3302586.330258712:4(203-213)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.5555/3302586.3302587

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents