Abstract
By construction, a video is a series of ordered frames, whose order is defined at the time of the acquisition process. Background subtraction methods then take this input video and produce a series of segmentation maps expressed in terms of foreground objects and scene background. To our knowledge, this natural ordering of frames has never been questioned or challenged.
In this paper, we propose to challenge, in a prospective view, the natural ordering of video frames in the context of background subtraction, and examine alternative time orderings. The idea consists in changing the order before background subtraction is applied, by means of shuffling strategies, and re-ordering the segmentation maps afterwards. For this purpose, we propose several shuffling strategies and show that, for some background subtraction methods, results are preserved or even improved. The practical advantage of time shuffling is that it can been applied to any existing background subtraction seamlessly.
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Laugraud, B., Latour, P., Van Droogenbroeck, M. (2015). Time Ordering Shuffling for Improving Background Subtraction. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_6
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DOI: https://doi.org/10.1007/978-3-319-25903-1_6
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