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• W3200748385 abstract "A basic algorithmic task in automated video surveillance is to separate background and foreground objects. Camera tampering, noisy videos, low frame rate, etc., pose difficulties in solving the problem. A general approach which classifies the tampered frames, and performs subsequent analysis on the remaining frames after discarding the tampered ones, results in loss of information. We propose a robust singular value decomposition (SVD) approach based on the density power divergence to perform background separation robustly even in the presence of tampered frames. We also provide theoretical results and perform simulations to validate the superiority of the proposed method over the few existing robust SVD methods. Finally, we indicate several other use-cases of the proposed method to show its general applicability to a large range of problems." @default.
• W3200748385 created "2021-09-27" @default.
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• W3200748385 date "2021-09-22" @default.
• W3200748385 modified "2023-10-09" @default.
• W3200748385 title "A New Robust Scalable Singular Value Decomposition Algorithm for Video Surveillance Background Modelling" @default.
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• W3200748385 hasPublicationYear "2021" @default.
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