Spatiotemporal local compact binary pattern for background subtraction in complex scenes

A variety of binary feature descriptors such as local binary pattern (LBP) and its variations have recently attracted considerable attention for modelling backgrounds, due to their robustness and strong discriminatory power. However, most existing binary feature descriptors fail to model complex scenes due to their sensitivity to noise. In this paper, we propose an effective local compact binary descriptor for background modelling. For each image, local compact binary patterns (LCBPs) are first extracted by computing a number of low-dimensional pixel difference vectors (PDVs). Then, the LCBP is extended to the spatiotemporal domain taking into account the temporal persistence of pixels, and a novel local compact binary descriptor, STLCBP, is proposed. Multiple color spaces are also considered in order to separate foreground from background pixels accurately. Finally, a joint domain-range adaptive kernel density estimate (KDE) model is used to estimate the background and foreground scores by combining texture features with color features. Experimental results on two well-known datasets, I2R and CDnet2014, demonstrate that the proposed approach significantly outperforms many state-of-the-art methods and works effectively on a wide range of complex videos.