Abstract
We present a new set of rapid detection of background subtraction algorithms using codebooks to established Background Model (BG Model) and the concept of Color Model originally proposed by [6]. The proposed methods do not require prior learning, as in [6], and can create an instant BG Model detection and training with instant learning mechanism. Our proposed methods can also turn the latter coming but stationary foreground objects gradually as background, which are more adaptive to the actual environments. The proposed methods can also use instant learning to absorb sudden camera movements caused by the environments. We show that the proposed methods are effective and efficient in video surveillance applications.
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Tang, CY., Wu, YL., Chao, SP., Chen, WC., Chen, PL. (2009). Anomaly Foreground Detection through Background Learning in Video Surveillance. In: Nakamatsu, K., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds) New Advances in Intelligent Decision Technologies. Studies in Computational Intelligence, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00909-9_41
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DOI: https://doi.org/10.1007/978-3-642-00909-9_41
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