| 摘要 |
To construct a video surveillance system, the Gaussian mixture modeling (GMM) is a popular choice among various background modeling approaches, for its capability of adaptation to background variations. However, the GMM often suffers from a tradeoff between robustness to background changes and sensitivity to foreground abnormalities, and is inefficient in managing the tradeoff for diverse surveillance scenarios. In the present invention, we identify that such a tradeoff can be easily controlled by a new computational scheme of two-type learning rate control for the GMM. Based on the proposed rate control scheme, a new video surveillance system that applies feedbacks of pixel properties computed in object-level analysis to the learning rate controls of the GMM in pixel-level background modeling is developed. Such a system gives better regularization of background adaptation and is efficient in resolving the tradeoff for many surveillance applications.
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