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Adaptive parametric statistical background subtraction for video segmentation

Published: 11 November 2005 Publication History

Abstract

The Background Subtraction Algorithm has been proven to be a very effective technique for automated video surveillance applications. In statistical approach, background model is usually estimated using Gaussian model and is adaptively updated to deal with changes in dynamic scene environment. However, most algorithms update background parameters linearly. As a result, the classification results are erroneous when performing background convergence process. In this paper, we present a novel learning factor control for adaptive background subtraction algorithm. The method adaptively adjusts the rate of adaptation in background model corresponding to events in video sequence. Experimental results show the algorithm improves classification accuracy compared to other known methods.

References

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T. Thongkamwitoon, S. Aramvith and T.H. Chalidabongse, "An Adaptive Real-time Background Subtraction and Moving Shadows Detection," Proceeding of International Conference on Multimedia and Expo 2004 (ICME 2004).
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T. Thongkamwitoon, S. Aramvith and T.H. Chalidabongse, "Non-Linear Learning Factor Control for Statistical Adaptive Background Subtraction Algorithm," Proceeding of International Symposium on Circuits and Systems 2005 (ISCAS 2005), May 2005.
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Cited By

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  • (2022)Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT SurveillanceJournal of Healthcare Engineering10.1155/2022/25923652022(1-14)Online publication date: 28-Mar-2022
  • (2018)On the role and the importance of features for background modeling and foreground detectionComputer Science Review10.1016/j.cosrev.2018.01.00428(26-91)Online publication date: May-2018
  • (2010)Virtual Piano Design via Single-View Video Based on Multifinger Actions Recognition2010 3rd International Conference on Human-Centric Computing10.1109/HUMANCOM.2010.5563348(1-5)Online publication date: Aug-2010
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  1. Adaptive parametric statistical background subtraction for video segmentation

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    cover image ACM Conferences
    VSSN '05: Proceedings of the third ACM international workshop on Video surveillance & sensor networks
    November 2005
    168 pages
    ISBN:1595932429
    DOI:10.1145/1099396
    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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    New York, NY, United States

    Publication History

    Published: 11 November 2005

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    Author Tags

    1. adaptive background subtraction
    2. non-linear parameters update
    3. pixel classification
    4. unimodal distribution

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    MM&Sec '05
    MM&Sec '05: Multimedia and Security Workshop 2005
    November 11, 2005
    Hilton, Singapore

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    View all
    • (2022)Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT SurveillanceJournal of Healthcare Engineering10.1155/2022/25923652022(1-14)Online publication date: 28-Mar-2022
    • (2018)On the role and the importance of features for background modeling and foreground detectionComputer Science Review10.1016/j.cosrev.2018.01.00428(26-91)Online publication date: May-2018
    • (2010)Virtual Piano Design via Single-View Video Based on Multifinger Actions Recognition2010 3rd International Conference on Human-Centric Computing10.1109/HUMANCOM.2010.5563348(1-5)Online publication date: Aug-2010
    • (2007)Satellite imagery based adaptive background models and shadow suppressionSignal, Image and Video Processing10.1007/s11760-007-0013-81:2(119-132)Online publication date: 25-Apr-2007
    • (2006)"Hybrid Cone-Cylinder" Codebook Model for Foreground Detection with Shadow and Highlight SuppressionProceedings of the IEEE International Conference on Video and Signal Based Surveillance10.1109/AVSS.2006.1Online publication date: 22-Nov-2006

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