Abstract
Moving object segmentation is an important component of many vision systems, especially in the non-static background. This paper proposes an approach based on Kernel Density Estimation that can handle situations where the background of the scene is not completely static but contains significant stochastic motion (e.g. water). To get the initial results, a higher dimensional KDE model using the observing pixel intensity values and the information of optical flow is built. Then a KDE observing model based on the Hidden Markov Random Field Model and the Expectation Maximization frame work, is used for segmented the moving object. Experimental results show that the proposed approach can accurately detect moving objects and use less video frames.
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Acknowledgments
This work is partially supported by National Natural Science Foundation of China 61371175 and Fundamental Research Funds for the Central Universities HEUCFQ20150812.
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Qiao, Y., Xi, W. (2017). A Kernel Density Estimation Model for Moving Object Detection. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_63
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DOI: https://doi.org/10.1007/978-981-10-5041-1_63
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