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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Moving Objects Detection Using Adaptive Region-Based Background Model in Dynamic Scenes
verfasst von : Lin Gao, Yong Fan, Niannian Chen, Yufeng Li, Xiaorong Li
Erschienen in: Foundations of Intelligent Systems
Verlag: Springer Berlin Heidelberg
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Moving object detection plays an important role in video surveillance, yet in dynamic scenes it is still a challenging problem. In this work, we develop an efficient algorithm to handling complex dynamic backgrounds by using an adaptive region-based background model. Firstly, the initial background image is partitioned using image over-segmentation methods. Then the input frame is partitioned to image regions according to the obtained partition manner. Features of the image regions are used to construct adaptive mixture Gaussian models. When the background model is updating, the number of component of the mixture Gaussian models is selected adaptively based on the activity level of features. A coarse-to-fine strategy is designed to detect the moving object. The foreground and background are distinguished gradually in region-level and pixel-level through the built background model. Experimental results show that the algorithm proposed in this paper can detect moving objects quickly and effectively.