Background Subtraction for Dynamic Texture Scenes Using Local FCH features With Adaptive Updating Procedure

Abstract

Background subtraction is a very popular approach for vehicle detection in traffic surveillance systems. A conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interference such as illumination changes and quantization errors. Furthermore, CCHs large dimension or histogram bins requires large computation on histogram comparison. However, structured motion patterns of the background (e.g., Vehicular traffic videos, waving leaves, spouting fountain, rippling water, etc.), which are distinctive from variations due to noise, are hardly tolerated in this assumption. To address these concerns, we introduce a 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 using LFCH features with adaptive updating procedure compared to several competitive methods proposed in the literature.