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Background modeling is a fundamental problem in computer vision and usually as the first step for high-level applications. Pixel based approaches usually ignore the spatial coherence, while region based approaches are sensitive to region size and scene complexity. In this paper, we propose a robust background subtraction approach via multiple features based shared models. Each shared model is represented...
Background modeling and subtraction is a classical topic in compute vision. Gaussian mixture modeling (GMM) is a popular choice for its capability of adaptation to background variations. Lots of improvements have been made to enhance the robustness by considering spatial consistency and temporal correlation. In this paper, we propose a sharable GMM based background subtraction approach. Firstly, a...
In this paper, we propose a hierarchical approach for background modeling and moving objects detections in the intelligent visual surveillance system. The proposed approach models the background in block level and pixel level hierarchically, and the background is represented by texture information in block level and by color information in pixel level respectively. Meanwhile the variable parameters...
This paper presents a modified codebook model for real-time moving foreground detection. The proposed method is an effective combination of background modeling and motion detection. Without a long training sequence, the background model can be represented in a compressed form, a series of codebooks, which means sample background values for each pixel are quantized into codebooks that can used in detection...
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