Abstract
Recently, high-efficiency video coding becomes more and more demanded as the explosive requirements of network bandwidth and storage space for surveillance video applications. In this paper, we propose a background modeling scheme based on high efficiency motion classification. Firstly, pixels at each location are classified into three motion states, namely the static, the gentle motion and the severe motion states, according to the motion vectors of the corresponding current block and neighboring blocks. Then based on the classification and pixel differential value, the segmentation is performed for the co-located pixels in the training frames, and the mean pixel value of each segment can then be calculated. Finally, the background modeling frame can be obtained by an optimized weighted average of the segmented mean pixel values. Experimental results show that our proposed scheme achieves an average PSNR gain of 0.65dB than the AVS surveillance baseline video encoder, and it gets the best performance among several high efficiency background modeling methods in fast motion and large foreground sequences.
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Liao, P. et al. (2014). A Background Modeling Scheme Based on High Efficiency Motion Classification for Surveillance Video Coding. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, CK., Huet, B., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture Notes in Computer Science, vol 8879. Springer, Cham. https://doi.org/10.1007/978-3-319-13168-9_5
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DOI: https://doi.org/10.1007/978-3-319-13168-9_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13167-2
Online ISBN: 978-3-319-13168-9
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