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Proceedings Paper

Video object segmentation via adaptive threshold based on background model diversity
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Paper Abstract

The background subtraction could be presented as classification process when investigating the upcoming frames in a video stream, taking in consideration in some cases: a temporal information, in other cases the spatial consistency, and these past years both of the considerations above. The classification often relied in most of the cases on a fixed threshold value. In this paper, a framework for background subtraction and moving object detection based on adaptive threshold measure and short/long frame differencing procedure is proposed. The presented framework explored the case of adaptive threshold using mean squared differences for a sampled background model. In addition, an intuitive update policy which is neither conservative nor blind is presented. The algorithm succeeded on extracting the moving foreground and isolating an accurate background.

Paper Details

Date Published: 4 March 2015
PDF: 5 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 944329 (4 March 2015); doi: 10.1117/12.2179192
Show Author Affiliations
Mohamed Bachir Boubekeur, Beijing Institute of Technology (China)
SenLin Luo, Beijing Institute of Technology (China)
Hocine Labidi, Beijing Institute of Technology (China)
Tarek Benlefki, Beihang Univ. (China)

Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)

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