Randomized low-rank Dynamic Mode Decomposition for motion detection
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This paper introduces a fast algorithm for randomized computation of a low-rank Dynamic Mode Decomposition (DMD) of a matrix. Here we consider this matrix to represent the development of a spatial grid through time e.g. data from a static video source. DMD was originally introduced in the fluid mechanics community, but is also suitable for motion detection in video streams and its use for background subtraction has received little previous investigation. In this study we present a comprehensive evaluation of background subtraction, using the randomized DMD and compare the results with leading robust principal component analysis algorithms. The results are convincing and show the random DMD is an efficient and powerful approach for background modeling, allowing processing of high resolution videos in real-time. Supplementary materials include implementations of the algorithms in Python.
Erichson , N B & Donovan , C R 2016 , ' Randomized low-rank Dynamic Mode Decomposition for motion detection ' , Computer Vision and Image Understanding , vol. 146 , pp. 40-50 . https://doi.org/10.1016/j.cviu.2016.02.005
Computer Vision and Image Understanding
© 2016, Publisher / the Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at www.sciencedirect.com / https://dx.doi.org/10.1016/j.cviu.2016.02.005
DescriptionN. Benjamin Erichson acknowledges support from the UK Engineering and Physical Sciences Research Council (EPSRC).
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