Abstract
In this paper, a SVM regression based method is proposed for background estimation and foreground detection. Incoming frames are treated as time series and a fixed-scale working-set selecting strategy is specifically designed for real-time background estimation. Experiments on two representat-ive videos demonstrate the application potential of the proposed algorithm and also reveal some problems underlying it. Both the positive and negative reports from our study offer some useful information for researchers both in the field of image processing and that of machine learning.
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Hao, Z., Wen, W., Liu, Z., Yang, X. (2007). Real-Time Foreground-Background Segmentation Using Adaptive Support Vector Machine Algorithm. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_62
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DOI: https://doi.org/10.1007/978-3-540-74695-9_62
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