Moving cast shadow detection using block nonnegative matrix factorization

• Autorzy: Yang X. , Liu D. , Zhou D. , Yang R.

Identyfikatory

DOI

10.24425/122103

• Języki publikacji: EN

Abstrakty

EN

In recent years, moving cast shadow detection has become a critical challenge in improving the accuracy of moving object detection in video surveillance. In this paper, we propose two novel moving cast shadow detection methods based on nonnegative matrix factorization (NMF) and block nonnegative matrix factorization (BNMF). First, the algorithm of moving cast shadow detection using NMF is given and the key points such as the determination of moving shadow areas and the choice of discriminant function are specified. Then BNMF are introduced so that the new training samples and new classes can be added constantly with lower computational complexity. Finally, the improved shadow detection method is detailed described according to BNMF. The effectiveness of proposed methods is evaluated in various scenes. Experimental results demonstrate that the method achieves high detection rate and outperforms several state-of-the-art methods.

Słowa kluczowe

EN

moving cast shadow detection; video surveillance; nonnegative matrix factorization; block nonnegative matrix factorization

PL

nadzór wideo; nieujemna faktoryzacja macierzy

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2018
• Tom: Vol. 66, nr 2
• Strony: 229--234
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Yang X.

• College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China

autor

Liu D.

• College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China

autor

Zhou D.

• College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China

autor

Yang R.

• Computer Science Department, University of Kentucky, Lexington, Kentucky 40506, USA

Bibliografia

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• [18] A. Sanin, C. Sanderson, and B.C. Lovell, “Improved shadow removal for robust person tracking in surveillance scenarios”, International Conference on Pattern Recognition. IEEEComputer Society, 141‒144 (2010).
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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).