{"title":"Real-time Tracking in Image Sequences based-on Parameters Updating with Temporal and Spatial Neighborhoods Mixture Gaussian Model","authors":"Hu Haibo, Zhao Hong","volume":43,"journal":"International Journal of Electrical and Computer Engineering","pagesStart":1038,"pagesEnd":1044,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9832","abstract":"Gaussian mixture background model is widely used in\r\nmoving target detection of the image sequences. However, traditional\r\nGaussian mixture background model usually considers the time\r\ncontinuity of the pixels, and establishes background through statistical\r\ndistribution of pixels without taking into account the pixels- spatial\r\nsimilarity, which will cause noise, imperfection and other problems.\r\nThis paper proposes a new Gaussian mixture modeling approach,\r\nwhich combines the color and gradient of the spatial information, and\r\nintegrates the spatial information of the pixel sequences to establish\r\nGaussian mixture background. The experimental results show that the\r\nmovement background can be extracted accurately and efficiently, and\r\nthe algorithm is more robust, and can work in real time in tracking\r\napplications.","references":"[1] Wren, R. Christopher, A. Azarbayejani, T. Darrell and A, Pentland,\r\n\"Pfinder: real-time tracking of the human body\", IEEE Transactions on\r\nPattern Analysis and Machine Intelligence, 1997, 19(7), pp.780-785.\r\n[2] A. 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