Local Compact Binary Patterns for Background Subtraction in Complex Scenes
Wei Ping He, Yongkwan Kim, Jianhui Wu, Guoyun Zhang, Qi Qi, Longyuan Guo, Bing Tu, Feng Chen Huang · 2018
Background modeling in complex scenes is a challenging problem. In this paper, a novel background subtraction method is proposed to address it. First, the textures are modeled with local compact binary patterns (LCBP), which have excellent robustness, strong discriminative power, and fast computation speed. To make LCBP more effective to appearance changes in complex scenarios, spatiotemporal local compact binary patterns (STLCBP) are then considered in which spatial texture information and temporal motion information are combined together. Multiple color spaces are also presented to separate foreground pixels more accurately from the background. To our knowledge, this is the first time that LCBP have been used for background modeling. Extensive experimental results on a widely used dataset clearly show that the proposed method outperforms other state-of-the-art methods and works effectively in complex scenes.