Abstract
Background subtraction has become a popular method for video-based motion detection. In this chapter, we present a novel statistic parametric model by doing statistical analysis for history samples, incorporating the parameters of the sample number forming the models, the sampling time center and the last time point, which are ignored by existing background models. With these parameters, the model can be updated in time and accurately. The experimental results show that the presented model can suppress false detections from tail phenomenon, shadows, illumination change, repetitive motion, cluttered areas, and so on.
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Acknowledgment
The work is supported by National Natural Science Foundation of China (Grant No: 61163024 and 61170222), Scientific Research Foundation from department of education of Yunnan Province of China (Grant No: 2011Y118), and Scientific and Technology Research Foundation of Yunnan University (Grant No: 2010YB027).
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Gao, Y., Zhou, H., Zhang, X. (2014). Detecting Motion Regions Using Statistic Parameters. In: Xing, S., Chen, S., Wei, Z., Xia, J. (eds) Unifying Electrical Engineering and Electronics Engineering. Lecture Notes in Electrical Engineering, vol 238. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4981-2_128
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DOI: https://doi.org/10.1007/978-1-4614-4981-2_128
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