research-article

Abnormal Activity Detection based on Dense Spatial-Temporal Features and Improved One-Class Learning

Authors:

Ngoc Q. LyAuthors Info & Claims

SoICT '17: Proceedings of the 8th International Symposium on Information and Communication Technology

Pages 370 - 377

https://doi.org/10.1145/3155133.3155147

Published: 07 December 2017 Publication History

Abstract

Abnormal activity detection is an important issue in video surveillance. The abnormal activity could be a predictable activity or unpredictable activity. This paper focuses on unpredictable activity detection. Due to unpredictable anomalies, we do not have training data of them, so we could not use the discriminative learning model to detect abnormal activity and normal activity. One class learning method is the generative model and it is suitable to model unpredictable abnormal activities. In this paper, we use fast dense spatial-temporal features within regions of interest points to model normal activities by Support Vector Data Description (SVDD). Besides. we use K-means++ algorithm to cluster normal data then the multi hyperspheres SVDD are constructed separately on clusters instead of only one hypersphere SVDD on multi-distribution data. Experiments on benchmark datasets contain various situations with human crowds, overlapping between individual subjects and low resolution. The experiments show that our approach could outperform some state of the art methods on the Ped2 dataset.

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Cited By

Aquib Ansari MSingh DSingh V(2023)Detecting abnormal behavior in megastore for intelligent surveillance through 3D deep convolutional modelJournal of Electrical Engineering10.2478/jee-2023-002074:3(140-153)Online publication date: 22-Jul-2023
https://doi.org/10.2478/jee-2023-0020
Ansari MSingh D(2023)Optimized Parameter Tuning in a Recurrent Learning Process for Shoplifting Activity ClassificationCybernetics and Information Technologies10.2478/cait-2023-000823:1(141-160)Online publication date: 25-Mar-2023
https://doi.org/10.2478/cait-2023-0008
Ansari MSingh DSingh V(2023)Detecting abnormal behavior in megastore for crime prevention using a deep neural architectureInternational Journal of Multimedia Information Retrieval10.1007/s13735-023-00289-212:2Online publication date: 10-Aug-2023
https://doi.org/10.1007/s13735-023-00289-2
Show More Cited By

Index Terms

Abnormal Activity Detection based on Dense Spatial-Temporal Features and Improved One-Class Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene anomaly detection

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Published In

cover image ACM Other conferences

SoICT '17: Proceedings of the 8th International Symposium on Information and Communication Technology

December 2017

486 pages

ISBN:9781450353281

DOI:10.1145/3155133

General Chairs:
Huynh Quyet Thang
HUST, Vietnam
,
Zhenjiang Hu
NII, Japan
,
Program Chairs:
Marc Bui
EPHE, France
,
Biplab Sikdar
NUS, Singapore
,
Ichiro IDE
Nagoya, Japan
,
Huynh Thi Thanh Binh
HUST, Vietnam
,
Publications Chairs:
Worrawat Engchuan
Canada
,
Dinh Viet Sang
HUST, Vietnam
,
Nguyen Thi Oanh
HUST, Vietnam

Copyright © 2017 ACM.

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SOICT: School of Information and Communication Technology - HUST
NAFOSTED: The National Foundation for Science and Technology Development

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 December 2017

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SoICT 2017

SoICT 2017: The Eighth International Symposium on Information and Communication Technology

December 7 - 8, 2017

Nha Trang City, Viet Nam

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Overall Acceptance Rate 147 of 318 submissions, 46%

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Cited By

Aquib Ansari MSingh DSingh V(2023)Detecting abnormal behavior in megastore for intelligent surveillance through 3D deep convolutional modelJournal of Electrical Engineering10.2478/jee-2023-002074:3(140-153)Online publication date: 22-Jul-2023
https://doi.org/10.2478/jee-2023-0020
Ansari MSingh D(2023)Optimized Parameter Tuning in a Recurrent Learning Process for Shoplifting Activity ClassificationCybernetics and Information Technologies10.2478/cait-2023-000823:1(141-160)Online publication date: 25-Mar-2023
https://doi.org/10.2478/cait-2023-0008
Ansari MSingh DSingh V(2023)Detecting abnormal behavior in megastore for crime prevention using a deep neural architectureInternational Journal of Multimedia Information Retrieval10.1007/s13735-023-00289-212:2Online publication date: 10-Aug-2023
https://doi.org/10.1007/s13735-023-00289-2
Ansari MSingh D(2022)An expert video surveillance system to identify and mitigate shoplifting in megastoresMultimedia Tools and Applications10.1007/s11042-021-11438-281:16(22497-22525)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s11042-021-11438-2
Ansari MSingh D(2021)An Expert Eye for Identifying Shoplifters in Mega StoresInternational Conference on Innovative Computing and Communications10.1007/978-981-16-3071-2_10(107-115)Online publication date: 29-Aug-2021
https://doi.org/10.1007/978-981-16-3071-2_10
Vijeikis RRaudonis VDervinis G(2021)Towards Automated Surveillance: A Review of Intelligent Video SurveillanceIntelligent Computing10.1007/978-3-030-80129-8_53(784-803)Online publication date: 6-Jul-2021
https://doi.org/10.1007/978-3-030-80129-8_53
Ince I(2020)Performance Boosting of Scale and Rotation Invariant Human Activity Recognition (HAR) with LSTM Networks Using Low Dimensional 3D Posture Data in Egocentric CoordinatesApplied Sciences10.3390/app1023847410:23(8474)Online publication date: 27-Nov-2020
https://doi.org/10.3390/app10238474
İnce ÖInce IYıldırım MPark JSong JYoon B(2019)Human activity recognition with analysis of angles between skeletal joints using a RGB‐depth sensorETRI Journal10.4218/etrij.2018-057742:1(78-89)Online publication date: 13-Nov-2019
https://doi.org/10.4218/etrij.2018-0577
Gao JHuang XZhang L(2019)Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018Sustainability10.3390/su1123657411:23(6574)Online publication date: 21-Nov-2019
https://doi.org/10.3390/su11236574
Ashraf IHur SPark SPark Y(2019)DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble ClassifierSensors10.3390/s2001013320:1(133)Online publication date: 24-Dec-2019
https://doi.org/10.3390/s20010133

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