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Moving Objects Detection in Video by Various Background Modelling Algorithms and Score Fusion

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Intelligent Decision Technologies

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 309))

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Abstract

The paper presents results from testing ten of the fastest background modelling algorithms applied for detecting moving objects in video. The algorithms are Fast Principal Component Pursuit (Fast PCP), Grassmann Average (GA), Grassmann Median (GM), Go Decomposition (GoDec), Greedy Semi-Soft Go Decomposition (GreGoDec), Low-Rank Matrix Completion by Riemannian Optimization (LRGeomCG), Robust Orthonormal Subspace Learning (ROSL), Non-Negative Matrix Factorization via Nesterovs Optimal Gradient Method (NeNMF), Deep Semi Non-negative Matrix Factorization (Deep-Semi-NMF) and Tucker Decomposition by Alternating Least Squares (Tucker-ALS). Two new algorithms employing score fusion from Fast PCP and ROSL, which yielded alone the highest Detection Rate, Precision and F-measure, are proposed. The first algorithm has higher Detection Rate from all the others and the second—the highest Precision. Both are considered applicable in various practical scenarios when seeking either higher reliability of object detection or higher precision of the covered area by each object.

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Acknowledgements

This research was supported by the National Science Fund of Bulgaria [grant number KP-06-H27/16].

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Authors and Affiliations

  1. Technical University of Sofia, Sofia, Bulgaria

    Ivo Draganov & Rumen Mironov

Authors
  1. Ivo Draganov
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  2. Rumen Mironov
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Corresponding author

Correspondence to Ivo Draganov .

Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Gydnia, Poland

    Ireneusz Czarnowski

  2. ‘Aurel Vlaicu’ University of Arad, Arad, Romania

    Robert J. Howlett

  3. KES International, Selby, UK

    Lakhmi C. Jain

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Draganov, I., Mironov, R. (2022). Moving Objects Detection in Video by Various Background Modelling Algorithms and Score Fusion. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 309. Springer, Singapore. https://doi.org/10.1007/978-981-19-3444-5_30

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