abstract

Abnormal behavior recognition system for ATM monitoring by RGB-D camera

Authors:

Fan Liu,

Jinhui Tang,

Ruizhen Zhao,

Zhenmin TangAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 1295 - 1296

https://doi.org/10.1145/2393347.2396450

Published: 29 October 2012 Publication History

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Abstract

In this demo, we present an effective real-time system for ATM intelligent monitoring by using Kinect of Microsoft. With Kinect, we can easily detect people in ATM room and get their position information. By analyzing position information and video content, the system detects abnormal behaviors such as face-hiding, peeping and wandering, while records the time of these abnormal videos. Therefore, it not only prevents crimes but also helps to find suspects quickly after crimes have happened. The experimental results show that the system has the advantages of robustness, and provides a new mean for preventing financial crimes.

References

[1]

M. Yi. Abnormal Event Detection Method for ATM Video and Its Application. In Abnormal Event Detection Method for ATM Video and Its Application, 2011.

Crossref

Google Scholar

[2]

KinectSDK. http://research.microsoft.com/en-us/um/redmond/projects/kinectsdk/docs/ProgrammingGuide_KinectSDK.pdf.

Google Scholar

[3]

FaceSDK. http://www.luwand.com/download/Luxand_FaceSDK_Documentation.pdf.

Google Scholar

Cited By

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Dr. Sheshang Degadwala Patel Twinkleben Bharatbhai (2024)Advancements in ATM Security for Movement and Tampering Detection: A ReviewInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT241058710:5(79-89)Online publication date: 5-Sep-2024
https://doi.org/10.32628/CSEIT2410587
Kshirsagar AAzath H(2023)YOLOv3-based human detection and heuristically modified-LSTM for abnormal human activities detection in ATM machineJournal of Visual Communication and Image Representation10.1016/j.jvcir.2023.10390195:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.jvcir.2023.103901
Khaire PKumar P(2022)Online suspicious event detection in a constrained environment with RGB+D camera using multi-stream CNNs and SVMMultimedia Tools and Applications10.1007/s11042-022-12656-y81:23(32857-32881)Online publication date: 15-Apr-2022
https://doi.org/10.1007/s11042-022-12656-y
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Index Terms

Abnormal behavior recognition system for ATM monitoring by RGB-D camera
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Information & Contributors

Information

Published In

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
,
Xian-Sheng Hua
Microsoft, USA
,
Rong Yan
Facebook, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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MM '12

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MM '12: ACM Multimedia Conference

October 29 - November 2, 2012

Nara, Japan

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

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Dr. Sheshang Degadwala Patel Twinkleben Bharatbhai (2024)Advancements in ATM Security for Movement and Tampering Detection: A ReviewInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT241058710:5(79-89)Online publication date: 5-Sep-2024
https://doi.org/10.32628/CSEIT2410587
Kshirsagar AAzath H(2023)YOLOv3-based human detection and heuristically modified-LSTM for abnormal human activities detection in ATM machineJournal of Visual Communication and Image Representation10.1016/j.jvcir.2023.10390195:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.jvcir.2023.103901
Khaire PKumar P(2022)Online suspicious event detection in a constrained environment with RGB+D camera using multi-stream CNNs and SVMMultimedia Tools and Applications10.1007/s11042-022-12656-y81:23(32857-32881)Online publication date: 15-Apr-2022
https://doi.org/10.1007/s11042-022-12656-y
Sanas PKasbe PShelke ASalunkhe S(2021)Anomaly Detection at ATM Center using Machine Learning AlgorithmInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-974(230-236)Online publication date: 15-Apr-2021
https://doi.org/10.48175/IJARSCT-974
Khaire PKumar P(2021)RGB+D and deep learning-based real-time detection of suspicious event in Bank-ATMsJournal of Real-Time Image Processing10.1007/s11554-021-01155-2Online publication date: 23-Jul-2021
https://doi.org/10.1007/s11554-021-01155-2
Tian DXu XTao YWang X(2017)An Improved Activity Recognition Method Based on Smart Watch Data22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)10.1109/CSE-EUC.2017.148(756-759)Online publication date: Jul-2017
https://doi.org/10.1109/CSE-EUC.2017.148
Kazuki MTsutomu MDaiki H(2015)A Study of Laundry Tidiness: Socks Pairing Using Video and 3D SensorsIntelligence in the Era of Big Data10.1007/978-3-662-46742-8_28(303-313)Online publication date: 2015
https://doi.org/10.1007/978-3-662-46742-8_28

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Kinect-based Taiwanese sign-language recognition system

Multi-scenario gesture recognition using Kinect

TS-AUBD: A Novel Two-Stage Method for Abnormal User Behavior Detection

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