Abstract
Video surveillance is a widely used technology. Moving object detection is the most important content of video surveillance. Background modeling is an important method in moving object detection. However, background modeling algorithm is usually computationally intensive when the size of video is large. Kernel density estimation method based on Chebyshev inequality (KDSBC) is a new background modeling algorithm. This paper present MRKDSBC based on MapReduce which is a distributed programming model. Further more, we prove the correctness of the algorithm theoretically and implement it on Hadoop platform. Finally, we compare it with traditional algorithm.
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Wan, C., Wang, C., Zhang, K. (2012). MRKDSBC: A Distributed Background Modeling Algorithm Based on MapReduce. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_75
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DOI: https://doi.org/10.1007/978-3-642-31346-2_75
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