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Background subtraction is a very popular approach for detecting moving objects from a still scene. For this, most of previous methods depend on the assumption that the background is static over short time periods. However, structured motion patterns of the background (e.g., waving leaves, spouting fountain, rippling water, etc.), which are distinctive from variations due to noise, are hardly tolerated in this assumption and thus still lead to high-level false positive rates when using previous models. In this letter, we introduce a novel background subtraction algorithm for temporally dynamic texture scenes. Specifically, we propose to adopt a clustering-based feature, called fuzzy color histogram (FCH), which has an ability of greatly attenuating color variations generated by background motions while still highlighting moving objects. Experimental results demonstrate that the proposed method is effective for background subtraction in dynamic texture scenes compared to several competitive methods proposed in the literature.
Date of Publication: March 2012