Abstract
In video surveillance, the detected foreground is deformed or shapeless in cluttered or dynamic backgrounds. For this propose, we present an improvement method that can work in real time for silhouette determination of moving objects and dynamic background construction in such conditions. At first, image sequences are analysed pixel by pixel using improved basic sequential clustering algorithm. Then, clusters having high weight are used to estimate the background and pixels not belonging to the background clusters are labeled as foreground. Finally, the extracted foreground is treated from possible noises in a Markov Random Field framework. The original method is improved from the ghost effect problem, which drops some regions from the esteemed background and appears them as detected foreground, by adding clusters keeping the past stat of those regions from deviation to foreground detection. In addition, space memory is optimised by deleting old clusters that not updating after a timeout. The experiments show better results than the classical method in cluttered and multi background circumstances.
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Benalia, M., Ait-Aoudia, S. (2012). An Improved Basic Sequential Clustering Algorithm for Background Construction and Motion Detection. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31295-3_26
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DOI: https://doi.org/10.1007/978-3-642-31295-3_26
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