Title: Detection of change in video based on local pattern and photometric features
Authors: Chan, K L
Citation: WSCG '2018: short communications proceedings: The 26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech Republic May 28 - June 1 2018, p. 174-182.
Issue Date: 2018
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
conferenceObject
URI: wscg.zcu.cz/WSCG2018/!!_CSRN-2802.pdf
http://hdl.handle.net/11025/34670
ISBN: 978-80-86943-41-1
Keywords: modelování pozadí;detekce změny;místní ternární vzor;schéma příčného kanálu;pravděpodobnostní zdokonalení popředí
Keywords in different language: background modeling;change detection;local ternary pattern;cross-channel pattern;probabilistic foreground refinement
Abstract in different language: The segmentation of moving objects in video can be formulated as a background subtraction problem – the detection of change in each image frame. The background scene is learned and modeled. A pixelwise process is employed to determine whether the current pixel is similar or not to the background model. The detection of change in video is challenging due to the non-stationary background such as illumination change, background motions, etc. We propose new features for background modeling. Perception-based local ternary patterns are generated from the same color channels as well as from different color channels. Features computed from the local patterns are stored in the background model as samples. If the current pixel is classified as background, the background model is updated. Finally, we propose a probabilistic refinement to improve each change region by taking into account the spatially consistency of image features. We compare our method with various background subtraction algorithms on some video datasets. Our method can achieve 13% better performance than other methods.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG '2018: Short Papers Proceedings

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