research-article

Environmentally robust motion detection for video surveillance

Authors:

Jeong-Gyoo Kim,

Jin Keun SeoAuthors Info & Claims

IEEE Transactions on Image Processing, Volume 19, Issue 11

Pages 2838 - 2848

https://doi.org/10.1109/TIP.2010.2050644

Published: 01 November 2010 Publication History

Abstract

Most video surveillance systems require to manually set a motion detection sensitivity level to generate motion alarms. The performance of motion detection algorithms, embedded in closed circuit television (CCTV) camera and digital video recorder (DVR), usually depends upon the preselected motion sensitivity level, which is expected to work in all environmental conditions. Due to the preselected sensitivity level, false alarms and detection failures usually exist in video surveillance systems. The proposed motion detection model based upon variational energy provides a robust detection method at various illumination changes and noise levels of image sequences without tuning any parameter manually. We analyze the structure mathematically and demonstrate the effectiveness of the proposed model with numerous experiments in various environmental conditions. Due to the compact structure and efficiency of the proposed model, it could be implemented in a small embedded system.

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Cited By

Lv XZhang SWang CZhang WYao HHuang Q(2023)Unsupervised Low-Light Video Enhancement With Spatial-Temporal Co-Attention TransformerIEEE Transactions on Image Processing10.1109/TIP.2023.330133232(4701-4715)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3301332
Zhenjie Zhao Xuebo Zhang Yongchun Fang (2015)Stacked Multilayer Self-Organizing Map for Background ModelingIEEE Transactions on Image Processing10.1109/TIP.2015.242751924:9(2841-2850)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1109/TIP.2015.2427519
Zhao XLiu PLiu JTang X(2013)Removal of dynamic weather conditions based on variable time windowIET Computer Vision10.1049/iet-cvi.2012.01317:4(219-226)Online publication date: 1-Aug-2013
https://dl.acm.org/doi/10.1049/iet-cvi.2012.0131

Environmentally robust motion detection for video surveillance
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Computer graphics
    1. Animation

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Information

Published In

cover image IEEE Transactions on Image Processing

IEEE Transactions on Image Processing Volume 19, Issue 11

November 2010

280 pages

ISSN:1057-7149

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Copyright © 2010.

Publisher

IEEE Press

Publication History

Published: 01 November 2010

Accepted: 20 April 2010

Revised: 18 January 2010

Received: 24 September 2009

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Cited By

Lv XZhang SWang CZhang WYao HHuang Q(2023)Unsupervised Low-Light Video Enhancement With Spatial-Temporal Co-Attention TransformerIEEE Transactions on Image Processing10.1109/TIP.2023.330133232(4701-4715)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3301332
Zhenjie Zhao Xuebo Zhang Yongchun Fang (2015)Stacked Multilayer Self-Organizing Map for Background ModelingIEEE Transactions on Image Processing10.1109/TIP.2015.242751924:9(2841-2850)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1109/TIP.2015.2427519
Zhao XLiu PLiu JTang X(2013)Removal of dynamic weather conditions based on variable time windowIET Computer Vision10.1049/iet-cvi.2012.01317:4(219-226)Online publication date: 1-Aug-2013
https://dl.acm.org/doi/10.1049/iet-cvi.2012.0131

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