@inproceedings{6d299b27e4914957aeb08fe937561c57,
title = "Video object segmentation via adaptive threshold based on background model diversity",
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..",
keywords = "Background Subtraction, square successive differences, surveillance, video object segmentation",
author = "Bachir, {Boubekeur Mohamed} and Luo Senlin and Labidi Hocine and Benlefki Tarek",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; 6th International Conference on Graphic and Image Processing, ICGIP 2014 ; Conference date: 24-10-2014 Through 26-10-2014",
year = "2015",
doi = "10.1117/12.2179192",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "David Zhang and Yulin Wang and Xudong Jiang",
booktitle = "Sixth International Conference on Graphic and Image Processing, ICGIP 2014",
address = "United States",
}