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60 Hz self-tuning background modeling

Published: 19 August 2015 Publication History

Abstract

Background modeling or change detection is often used as a preprocessing step in many computer vision tasks especially for intelligent surveillance. Despite various methods have been proposed to deal with this problem, they often involve complex parameter settings and have poor adaptability to scene changes. In this paper, we propose a fast and robust approach for background modeling with self-adaptive ability. Like ViBe [7], each pixel model is represented by a sequence of historical samples based on sample consensus. To adapt various changes in complex scenes, a flexible feedback scheme is presented to automatically adjust the model parameters. Moreover, a selective diffusion method is employed to overcome the problems like incomplete foregrounds or false detections brought by intermittent moving objects. Experiment results on ChangeDetection benchmark 2014 show that the proposed approach outperforms state-of-the-art approaches with a speed of 60 fps on CPU for a 640 × 480 image sequence.

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Published In

cover image ACM Other conferences
ICIMCS '15: Proceedings of the 7th International Conference on Internet Multimedia Computing and Service
August 2015
397 pages
ISBN:9781450335287
DOI:10.1145/2808492
  • General Chairs:
  • Ramesh Jain,
  • Shuqiang Jiang,
  • Program Chairs:
  • John Smith,
  • Jitao Sang,
  • Guohui Li
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: 19 August 2015

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Author Tags

  1. background modeling
  2. change detection

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ICIMCS '15

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ICIMCS '15 Paper Acceptance Rate 20 of 128 submissions, 16%;
Overall Acceptance Rate 163 of 456 submissions, 36%

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