Abstract
A new segmentation strategy is proposed to precisely extract moving objects in video sequences. It is based on the automatic detection of the static elements, and its classification as background and foreground using static differences and contextual information. Additionally, tracking information is incorporated to reduce the computational cost. Finally, segmentation is refined through a Markov random field (MRF) change detection analysis including the foreground information, which allows improving the accuracy of the segmentation. This strategy is presented in the context of low quality sequences of surveillance applications but it could be applied to other applications, the only requirement being to have a static or quasi static background.
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© 2005 Springer-Verlag Berlin Heidelberg
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Isenegger, L., Salgado, L., García, N. (2005). Moving Objects Segmentation Based on Automatic Foreground / Background Identification of Static Elements. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_62
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DOI: https://doi.org/10.1007/11558484_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
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