Non-static backgrounds modeling including high traffic regions

Park Daeyong, Byun Hyeran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

For the detection of moving objects in surveillance systems, background subtraction methods are widely used. In case the background is non-stationary, modeling the background is not a simple problem. To solve the problem, many methods are proposed. In the high traffic region such as airport and subways, however, few researches have been conducted. In this paper, we classify each pixel into four different types: still background, dynamic background, and moving object, and temporary still object. And update the background according to the result. For the classification, we analyze the temporal characteristics of each pixel's intensity with likelihood test. With public video data, we experimentally show that modeling based on pixel classification improves detection accuracy in public areas which has high traffic.

Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Pages3423-3427
Number of pages5
DOIs
Publication statusPublished - 2008 Dec 29
Event7th International Conference on Machine Learning and Cybernetics, ICMLC - Kunming, China
Duration: 2008 Jul 122008 Jul 15

Publication series

NameProceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Volume6

Other

Other7th International Conference on Machine Learning and Cybernetics, ICMLC
CountryChina
CityKunming
Period08/7/1208/7/15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Control and Systems Engineering

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  • Cite this

    Daeyong, P., & Hyeran, B. (2008). Non-static backgrounds modeling including high traffic regions. In Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC (pp. 3423-3427). [4620996] (Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC; Vol. 6). https://doi.org/10.1109/ICMLC.2008.4620996