short-paper

Unsupervised fast anomaly detection in crowds

Authors:

Pengfei XuAuthors Info & Claims

MM '11: Proceedings of the 19th ACM international conference on Multimedia

Pages 1469 - 1472

https://doi.org/10.1145/2072298.2072042

Published: 28 November 2011 Publication History

Abstract

In this paper, we proposed a fast and robust unsupervised framework for anomaly detection and localization in crowed scenes. Our method avoids modeling the normal state of the crowds which is a very complex task due to the large within class variance of the normal target appearance and motion patterns. For each video frame, we extract the spatial temporal features of 3D blocks and generate the saliency map using a block-based center-surround difference operator. Then, motion vector matrix is obtained by adaptive rood pattern search block-matching algorithm and distance normalization. Attractive motion disorder descriptor is proposed to measure the global intensity of anomalies in the scene. Finally, we classify the frames into normal and anomalous ones by a binary classifier. In the experiments, we compared our method against several state-of-the-art approaches on UCSD dataset which is a widely used anomaly detection and localization benchmark. As the only unsupervised approach, our method outputs competitive results with near real-time processing speed

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Cited By

Amudha JDivya KAarthi R(2020)A fuzzy based system for target search using top-down visual attentionJournal of Intelligent & Fuzzy Systems10.3233/JIFS-179712(1-13)Online publication date: 30-Mar-2020
https://doi.org/10.3233/JIFS-179712
Carneiro Sda Silva GGuimaraes SPedrini H(2019)Fight Detection in Video Sequences Based on Multi-Stream Convolutional Neural Networks2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI.2019.00010(8-15)Online publication date: Oct-2019
https://doi.org/10.1109/SIBGRAPI.2019.00010
Spire CChakrabarty D(2019)Learning in the Absence of Training Data—A Galactic ApplicationBayesian Statistics and New Generations10.1007/978-3-030-30611-3_5(43-51)Online publication date: 22-Nov-2019
https://doi.org/10.1007/978-3-030-30611-3_5
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Index Terms

Unsupervised fast anomaly detection in crowds
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. Information systems
  1. Information retrieval
    1. Document representation

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Information

Published In

cover image ACM Conferences

MM '11: Proceedings of the 19th ACM international conference on Multimedia

November 2011

944 pages

ISBN:9781450306164

DOI:10.1145/2072298

General Chairs:
K. Selçuk Candan
Arizona State University, USA
,
Sethuraman Panchanathan
Arizona State University, USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA
,
Program Chairs:
Hari Sundaram
Arizona State University, USA
,
Wu-Chi Feng
Portland State University, USA
,
Nicu Sebe
University of Trento, Italy

Copyright © 2011 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2011

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MM '11

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SIGMM

MM '11: ACM Multimedia Conference

November 28 - December 1, 2011

Arizona, Scottsdale, USA

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

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Cited By

Amudha JDivya KAarthi R(2020)A fuzzy based system for target search using top-down visual attentionJournal of Intelligent & Fuzzy Systems10.3233/JIFS-179712(1-13)Online publication date: 30-Mar-2020
https://doi.org/10.3233/JIFS-179712
Carneiro Sda Silva GGuimaraes SPedrini H(2019)Fight Detection in Video Sequences Based on Multi-Stream Convolutional Neural Networks2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI.2019.00010(8-15)Online publication date: Oct-2019
https://doi.org/10.1109/SIBGRAPI.2019.00010
Spire CChakrabarty D(2019)Learning in the Absence of Training Data—A Galactic ApplicationBayesian Statistics and New Generations10.1007/978-3-030-30611-3_5(43-51)Online publication date: 22-Nov-2019
https://doi.org/10.1007/978-3-030-30611-3_5
Ratre APankajakshan V(2018)Tucker tensor decomposition‐based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videosIET Computer Vision10.1049/iet-cvi.2017.046912:6(933-940)Online publication date: 12-Jun-2018
https://doi.org/10.1049/iet-cvi.2017.0469
Mi ZShang LXue B(2018)Multi-Dimensional Optical Flow Embedded Genetic Programming for Anomaly Detection in Crowded ScenesNeural Information Processing10.1007/978-3-030-04179-3_43(486-497)Online publication date: 18-Nov-2018
https://doi.org/10.1007/978-3-030-04179-3_43
Colque RCaetano Cde Andrade MSchwartz W(2017)Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in VideosIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2016.263777827:3(673-682)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1109/TCSVT.2016.2637778
Wang CYao HSun X(2017)Anomaly detection based on spatio-temporal sparse representation and visual attention analysisMultimedia Tools and Applications10.1007/s11042-015-3199-876:5(6263-6279)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s11042-015-3199-8
Biswas SGupta V(2017)Abnormality detection in crowd videos by tracking sparse componentsMachine Vision and Applications10.1007/s00138-016-0800-828:1-2(35-48)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s00138-016-0800-8
Wu YYe YZhao CZhou XSmeaton ATian QBulterman DShen HMayer-Patel KYan S(2015)Coherent Motion Detection with Collective Density ClusteringProceedings of the 23rd ACM international conference on Multimedia10.1145/2733373.2806227(361-370)Online publication date: 13-Oct-2015
https://dl.acm.org/doi/10.1145/2733373.2806227
Colque RJúnior CSchwartz W(2015)Histograms of Optical Flow Orientation and Magnitude to Detect Anomalous Events in VideosProceedings of the 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images10.1109/SIBGRAPI.2015.21(126-133)Online publication date: 26-Aug-2015
https://dl.acm.org/doi/10.1109/SIBGRAPI.2015.21
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