Moving object detection via robust background modeling with recurring patterns voting

Moving object detection via robust background modeling with recurring patterns voting This paper proposes a simple yet effective background modeling method based on recurring patterns voting for moving object detection under challenging scenes. Our method performs the following two steps. First, we employ Gaussian Mixture Model (GMM) to generate the initial background probability map for each frame, in which the value of each pixel represents its probability belonging to the background. Second, we perform recurring patterns voting by employing the graph-based manifold ranking algorithm on the spatially constrained graph to refine the probability map. This prior bases on the observation that the same patterns tend to recur frequently in same semantic regions (background or foreground). We verify it in our problem by calculating the background-background, foreground-foreground and background-foreground densities on some video sequences with ground truths. Experimental results on public video sequences suggest that the proposed method significantly outperforms other moving object detection methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Moving object detection via robust background modeling with recurring patterns voting

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4975-4
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a simple yet effective background modeling method based on recurring patterns voting for moving object detection under challenging scenes. Our method performs the following two steps. First, we employ Gaussian Mixture Model (GMM) to generate the initial background probability map for each frame, in which the value of each pixel represents its probability belonging to the background. Second, we perform recurring patterns voting by employing the graph-based manifold ranking algorithm on the spatially constrained graph to refine the probability map. This prior bases on the observation that the same patterns tend to recur frequently in same semantic regions (background or foreground). We verify it in our problem by calculating the background-background, foreground-foreground and background-foreground densities on some video sequences with ground truths. Experimental results on public video sequences suggest that the proposed method significantly outperforms other moving object detection methods.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jul 12, 2017

References

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