short-paper

Online Weighted Clustering for Real-time Abnormal Event Detection in Video Surveillance

Authors:

Jeremiah D. Deng,

Brendon J. Woodford,

Ahmad ShahiAuthors Info & Claims

MM '16: Proceedings of the 24th ACM international conference on Multimedia

Pages 536 - 540

https://doi.org/10.1145/2964284.2967279

Published: 01 October 2016 Publication History

Abstract

Detecting abnormal events in video surveillance is a challenging problem due to the large scale, stream fashion video data as well as the real-time constraint. In this paper, we present an online, adaptive, and real-time framework to address this problem. The spatial locations in a frame is partitioned into grids, in each grid the proposed Adaptive Multi-scale Histogram Optical Flow (AMHOF) features are extracted and modelled by an Online Weighted Clustering (OWC) algorithm. The AMHOFs which cannot be fit to a cluster with large weight are regarded as abnormal events. The OWC algorithm is simple to implement and computational efficient. In addition, we improve the detection performance by a Multiple Target Tracking (MTT) algorithm. Experimental results demonstrate our approach outperforms the state-of-the-art approaches in pixel-level rate of detection at a processing speed of 30 FPS.

References

[1]

B. Antić and B. Ommer. Video parsing for abnormality detection. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2415--2422. IEEE, 2011.

Digital Library

[2]

S. S. Blackman. Multiple hypothesis tracking for multiple target tracking. Aerospace and Electronic Systems Magazine, IEEE, 19(1):5--18, 2004.

[3]

M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. Lof: identifying density-based local outliers. In ACM sigmod record, volume 29, pages 93--104. ACM, 2000.

Digital Library

[4]

Y. Cong, J. Yuan, and J. Liu. Abnormal event detection in crowded scenes using sparse representation. Pattern Recognition, 46(7):1851--1864, 2013.

Digital Library

[5]

L. J. Cox and S. L. Hingorani. An efficient implementation of reid's multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 18(2):138--150, 1996.

Digital Library

[6]

B. K. Horn and B. G. Schunck. Determining optical flow. In 1981 Technical Symposium East, pages 319--331. International Society for Optics and Photonics, 1981.

Digital Library

[7]

R. E. Kalman. A new approach to linear filtering and prediction problems. Journal of basic Engineering, 82(1):35--45, 1960.

[8]

C. Kim, F. Li, A. Ciptadi, and J. M. Rehg. Multiple hypothesis tracking revisited. In Proceedings of the IEEE International Conference on Computer Vision, pages 4696--4704, 2015.

Digital Library

[9]

W. Li, V. Mahadevan, and N. Vasconcelos. Anomaly detection and localization in crowded scenes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(1):18--32, Jan 2014.

Digital Library

[10]

C. Lu, J. Shi, and J. Jia. Abnormal event detection at 150 fps in matlab. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 2720--2727. IEEE, 2013.

Digital Library

[11]

V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos. Anomaly detection in crowded scenes. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 1975--1981. IEEE, 2010.

[12]

B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 13(7):1443--1471, 2001.

Digital Library

[13]

C. Stauffer and W. E. L. Grimson. Adaptive background mixture models for real-time tracking. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., volume 2, pages 246--252. IEEE, 1999.

[14]

K. Suzuki, I. Horiba, and N. Sugie. Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding, 89(1):1--23, 2003.

Digital Library

[15]

Z. Zivkovic. Improved adaptive gaussian mixture model for background subtraction. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, volume 2, pages 28--31. IEEE, 2004.

Digital Library

Cited By

Ho ADoan HPham T(2024)Multi-Modality Abnormal Crowd Detection with Self-Attention and Knowledge DistillationEngineering, Technology & Applied Science Research10.48084/etasr.819414:5(16674-16679)Online publication date: 9-Oct-2024
https://doi.org/10.48084/etasr.8194
Dhurgadevi MVimal Kumar DSenthilkumar RGunasekaran K(2024)Detection of Video Anomaly in Public With Deep Learning AlgorithmMachine and Deep Learning Techniques for Emotion Detection10.4018/979-8-3693-4143-8.ch004(81-95)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-4143-8.ch004
Zhang YSong JJiang YLi H(2023)Online Video Anomaly DetectionSensors10.3390/s2317744223:17(7442)Online publication date: 26-Aug-2023
https://doi.org/10.3390/s23177442
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Index Terms

Online Weighted Clustering for Real-time Abnormal Event Detection in Video Surveillance

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Published In

cover image ACM Conferences

MM '16: Proceedings of the 24th ACM international conference on Multimedia

October 2016

1542 pages

ISBN:9781450336031

DOI:10.1145/2964284

General Chairs:
Alan Hanjalic
Delft University of Technology
,
Cees Snoek
Qualcomm Research Netherlands / University of Amsterdam
,
Marcel Worring
University of Amsterdam
,
Moderator:
Dick Bulterman
CWI / VU University Amsterdam
,
Program Chairs:
Benoit Huet
EURECOM
,
Aisling Kelliher
Virginia Tech
,
Yiannis Kompatsiaris
CERTH-ITI
,
Jin Li
Microsoft

Copyright © 2016 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]

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Association for Computing Machinery

New York, NY, United States

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Published: 01 October 2016

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MM '16: ACM Multimedia Conference

October 15 - 19, 2016

Amsterdam, The Netherlands

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MM '16 Paper Acceptance Rate 52 of 237 submissions, 22%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Ho ADoan HPham T(2024)Multi-Modality Abnormal Crowd Detection with Self-Attention and Knowledge DistillationEngineering, Technology & Applied Science Research10.48084/etasr.819414:5(16674-16679)Online publication date: 9-Oct-2024
https://doi.org/10.48084/etasr.8194
Dhurgadevi MVimal Kumar DSenthilkumar RGunasekaran K(2024)Detection of Video Anomaly in Public With Deep Learning AlgorithmMachine and Deep Learning Techniques for Emotion Detection10.4018/979-8-3693-4143-8.ch004(81-95)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-4143-8.ch004
Zhang YSong JJiang YLi H(2023)Online Video Anomaly DetectionSensors10.3390/s2317744223:17(7442)Online publication date: 26-Aug-2023
https://doi.org/10.3390/s23177442
Goncalves VSilva LNunes FFerreira JAraújo L(2023)Concept drift adaptation in video surveillance: a systematic reviewMultimedia Tools and Applications10.1007/s11042-023-15855-383:4(9997-10037)Online publication date: 20-Jun-2023
https://doi.org/10.1007/s11042-023-15855-3
Dube SBiradar KVipparthi STyagi D(2022)MAG-Net: A Memory Augmented Generative Framework for Video Anomaly Detection Using ExtrapolationComputer Vision and Image Processing10.1007/978-3-031-11349-9_37(426-437)Online publication date: 24-Jul-2022
https://doi.org/10.1007/978-3-031-11349-9_37
Rehman AUllah HFarooq HKhan MMahmood TKhan H(2021)Multi-Modal Anomaly Detection by Using Audio and Visual CuesIEEE Access10.1109/ACCESS.2021.30595199(30587-30603)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3059519
Ata-Ur-Rehman Tariq SFarooq HJaleel AWasif S(2021)Anomaly Detection With Particle Filtering for Online Video SurveillanceIEEE Access10.1109/ACCESS.2021.30540409(19457-19468)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3054040
Pang GYan CShen Cvan den Hengel ABai X(2020)Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR42600.2020.01219(12170-12179)Online publication date: Jun-2020
https://doi.org/10.1109/CVPR42600.2020.01219
Hu JZhu EWang SLiu XGuo XYin J(2019)An Efficient and Robust Unsupervised Anomaly Detection Method Using Ensemble Random Projection in Surveillance VideosSensors10.3390/s1919414519:19(4145)Online publication date: 24-Sep-2019
https://doi.org/10.3390/s19194145
Hu JZhu EWang SWang SLiu XYin J(2019)Two-stage Unsupervised Video Anomaly Detection using Low-rank based Unsupervised One-class Learning with Ridge Regression2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852022(1-8)Online publication date: Jul-2019
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