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Recently, most background modeling approaches represent distributions of background changes by using parametric models such as Gaussian mixture models. Because of significant illumination changes and dynamic moving backgrounds with time, variations of background changes are hard to be modeled by parametric background models. Moreover, how to efficiently and effectively update parameters of parametric...
In this paper, we propose a new binary descriptor based background modeling approach which is robust to lighting changes and dynamic backgrounds in the environment. Instead of using traditional parametric models, our background models are constructed by background instances using binary descriptors computed from observed backgrounds. As shown in the experiments, our method can achieve better foreground...
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