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Robust background subtraction via online robust PCA using image decomposition

Published: 05 October 2014 Publication History

Abstract

Accurate and efficient background subtraction is an important task in video surveillance system. The task becomes more critical when the background scene shows more variations, such as water surface, waving trees and lighting conditions, etc. Recently, Robust Principal Components Analysis (RPCA) shows a nice framework for moving object detection. The background sequence is modeled by a low-dimensional subspace called low-rank matrix and sparse error constitutes the foreground objects. But RPCA presents the limitations of computational complexity and memory storage due to batch optimization methods, as a result it is hard to apply for real-time system. To handle these challenges, this paper presents a robust background subtraction algorithm via Online Robust PCA (OR-PCA) using image decomposition. OR-PCA with image decomposition approach improves the accuracy of foreground detection and the computation time as well. Comprehensive simulations on challenging datasets such as Wallflower, I2R and Change Detection 2014 demonstrate that our proposed scheme significantly outperforms the state-of-the-art approaches and works effectively on a wide range of complex background scenes.

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    cover image ACM Conferences
    RACS '14: Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems
    October 2014
    386 pages
    ISBN:9781450330602
    DOI:10.1145/2663761
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    Published: 05 October 2014

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    Author Tags

    1. foreground detection
    2. image decomposition
    3. low-rank matrix
    4. online robust PCA
    5. system

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    RACS '14 Paper Acceptance Rate 59 of 251 submissions, 24%;
    Overall Acceptance Rate 393 of 1,581 submissions, 25%

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    • (2023)Crack detection in ultrahigh-performance concrete using robust principal component analysis and characteristic evaluation in the frequency domainStructural Health Monitoring10.1177/14759217231178457Online publication date: 24-Jun-2023
    • (2023)Perception Over Time: Temporal Dynamics for Robust Image Understanding2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW59228.2023.00599(5656-5665)Online publication date: Jun-2023
    • (2020)Simultaneous Reconstruction and Moving Object Detection From Compressive Sampled Surveillance VideosIEEE Transactions on Image Processing10.1109/TIP.2020.300469629(7590-7602)Online publication date: 2020
    • (2020)An adaptive background modeling for foreground detection using spatio-temporal featuresMultimedia Tools and Applications10.1007/s11042-020-09552-8Online publication date: 7-Sep-2020
    • (2018)Moving Object Detection via Robust Low-Rank and Sparse Separating with High-Order Structural Constraint2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM)10.1109/BigMM.2018.8499057(1-6)Online publication date: Sep-2018
    • (2018)Robust PCA Using Matrix Factorization for Background/Foreground SeparationIEEE Access10.1109/ACCESS.2018.28183226(18945-18953)Online publication date: 2018
    • (2018)Decomposition into low-rank plus additive matrices for background/foreground separationComputer Science Review10.1016/j.cosrev.2016.11.00123:C(1-71)Online publication date: 13-Dec-2018
    • (2018)Traffic Sensing and Assessing in Digital Transportation SystemsLinking and Mining Heterogeneous and Multi-view Data10.1007/978-3-030-01872-6_5(107-135)Online publication date: 27-Nov-2018
    • (2016)Stochastic RPCA for Background/Foreground SeparationHandbook of Robust Low-Rank and Sparse Matrix Decomposition10.1201/b20190-26(457-480)Online publication date: 16-Jun-2016
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