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Advanced motion detection for intelligent video surveillance systems

Published: 22 March 2010 Publication History

Abstract

In this paper, we propose a novel background subtraction method that makes use of spectral, spatial, and temporal features extracted from the video sequence in determination of the best background candidates for background modeling. As the final step of our process, the binary moving object detection mask is computed using prompt background subtraction with our proposed background model. The overall results of these analyses thus demonstrate that our proposed method substantially outperforms existing methods by an F1 metric accuracy rate increase of up to 79%.

References

[1]
Manzanera, A., and Richefeu, J. C., 2007. A New Motion Detection Algorithm Based on Σ--Δ Background Estimation. Pattern Recognit. Lett. (Feb. 2007), 320--328.
[2]
Oral, M., and Deniz, U., 2007. Centre of Mass model - A Novel Approach to Background Modelling for Segmentation of Moving Objects. Image Vis. Comput. 25 (Aug. 2007), 1365--1376.
[3]
Wang, W., Yang, J., and Gao, W., 2008. Modeling Background and Segmenting Moving Objects from Compressed Video. IEEE Trans. Circuits Syst. Video Technol. 18 (May 2008), 670--681.
[4]
Havasi, L., Szlavik, Z., and Sziranyi, T., 2007. Detection of Gait Characteristics for Scene Registration in Video Surveillance System. IEEE Trans. Image Process. 16 (Feb. 2007), 503--510.
[5]
Maddalena, L., and Petrosino, A., 2008. A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications. IEEE Trans. Image Process. 17 (Jul. 2008), 1168--1177.

Cited By

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  • (2023)Security standards for real time video surveillance and moving object tracking challenges, limitations, and future: a case studyMultimedia Tools and Applications10.1007/s11042-023-16629-783:10(30113-30144)Online publication date: 15-Sep-2023
  • (2022)ABGS Segmenter: pixel wise adaptive background subtraction and intensity ratio based shadow removal approach for moving object detectionThe Journal of Supercomputing10.1007/s11227-022-04972-979:7(7937-7969)Online publication date: 9-Dec-2022
  • (2021)An improved Gaussian Mixture Method based Background Subtraction Model for Moving Object Detection in Outdoor Scene2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)10.1109/ICECCT52121.2021.9616883(1-8)Online publication date: 15-Sep-2021
  • Show More Cited By

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cover image ACM Conferences
SAC '10: Proceedings of the 2010 ACM Symposium on Applied Computing
March 2010
2712 pages
ISBN:9781605586397
DOI:10.1145/1774088
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2010

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Author Tags

  1. background model
  2. motion detection
  3. quality analysis

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SAC'10
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SAC'10: The 2010 ACM Symposium on Applied Computing
March 22 - 26, 2010
Sierre, Switzerland

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SAC '10 Paper Acceptance Rate 364 of 1,353 submissions, 27%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

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Cited By

View all
  • (2023)Security standards for real time video surveillance and moving object tracking challenges, limitations, and future: a case studyMultimedia Tools and Applications10.1007/s11042-023-16629-783:10(30113-30144)Online publication date: 15-Sep-2023
  • (2022)ABGS Segmenter: pixel wise adaptive background subtraction and intensity ratio based shadow removal approach for moving object detectionThe Journal of Supercomputing10.1007/s11227-022-04972-979:7(7937-7969)Online publication date: 9-Dec-2022
  • (2021)An improved Gaussian Mixture Method based Background Subtraction Model for Moving Object Detection in Outdoor Scene2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)10.1109/ICECCT52121.2021.9616883(1-8)Online publication date: 15-Sep-2021
  • (2021)A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engineInternational Journal of Intelligent Systems10.1002/int.2268137:11(9620-9654)Online publication date: 19-Sep-2021
  • (2019)Object Motion Detection Methods for Real-Time Video Surveillance: A Survey with Empirical EvaluationSmart Systems and IoT: Innovations in Computing10.1007/978-981-13-8406-6_63(663-679)Online publication date: 27-Oct-2019
  • (2018)A Background Subtraction Algorithm in Complex Environments Based on Category Entropy AnalysisApplied Sciences10.3390/app80608858:6(885)Online publication date: 28-May-2018
  • (2016)A cellular logic array based data mining framework for object detection in video surveillance system2016 2nd International Conference on Next Generation Computing Technologies (NGCT)10.1109/NGCT.2016.7877505(719-724)Online publication date: Oct-2016
  • (2016)Automatic moving object segmentation methods under varying illumination conditions for video dataMultimedia Tools and Applications10.1007/s11042-015-2927-475:23(16209-16264)Online publication date: 1-Dec-2016
  • (2014)Automatic Moving Object Extraction Through a Real-World Variable-Bandwidth Network for Traffic Monitoring SystemsIEEE Transactions on Industrial Electronics10.1109/TIE.2013.226276461:4(2099-2112)Online publication date: Apr-2014
  • (2013)Highly Accurate Moving Object Detection in Variable Bit Rate Video-Based Traffic Monitoring SystemsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2013.227031424:12(1920-1931)Online publication date: Dec-2013
  • Show More Cited By

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