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Enhanced extraction of moving objects in variable bit-rate video streams

Published: 29 October 2012 Publication History

Abstract

Motion detection plays an important role in the video surveillance system. Video communications over wireless networks can easily suffer from network congestion or unstable bandwidth, especially for embedded application. A rate control scheme produces various bit-rate video streams to match the available network bandwidth. However, effective detection of moving objects in various bit-rate video streams is a very difficult problem. This paper proposes an advanced approach based on the counter-propagation network through artificial neural networks to achieve effective moving object detection in various bit-rate video streams. We compare our method with other state-of-the-art methods. To demonstrate the performance of our proposed method in regard to object extraction, we analyze qualitative and quantitative comparisons in real-world limited bandwidth networks over a wide range of natural video sequences. The overall results show that our proposed method substantially outperforms other state-of-the-art methods by Similarity and F1 accuracy rates of 73.84% and 84.94%, respectively.

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Manzanera, A., and Richefeu, J. C., 2004. A robust and computationally efficient motion detection algorithm based on £-Δ background estimation. Proc. ICVGIP'04, 46--51.
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Zhou, D., and Zhang, H., 2005. Modified GMM background modeling and optical flow for detection of moving objects. Int. Conf. on Systems, Man, and Cybernetics 3, 2224--2229.
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Published In

cover image ACM Conferences
MM '12: Proceedings of the 20th ACM international conference on Multimedia
October 2012
1584 pages
ISBN:9781450310895
DOI:10.1145/2393347
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: 29 October 2012

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Author Tags

  1. motion detection
  2. neural network
  3. variable bit-rate
  4. video surveillance

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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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