Video object segmentation via adaptive threshold based on background model diversity

Mohamed Bachir Boubekeur; SenLin Luo; Hocine Labidi; Tarek Benlefki

doi:10.1117/12.2179192

4 March 2015 Video object segmentation via adaptive threshold based on background model diversity

Mohamed Bachir Boubekeur, SenLin Luo, Hocine Labidi, Tarek Benlefki

Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 944329 (2015) https://doi.org/10.1117/12.2179192
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China

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.

Citation Download Citation

Mohamed Bachir Boubekeur, SenLin Luo, Hocine Labidi, and Tarek Benlefki "Video object segmentation via adaptive threshold based on background model diversity", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 944329 (4 March 2015); https://doi.org/10.1117/12.2179192

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KEYWORDS

Statistical modeling

Video

Video surveillance

Image processing

Electronics

Surveillance

Systems modeling

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