Hierarchical Bayer-pattern based background subtraction for low resource devices

Automatic visual monitoring is one of the key research areas in recent years. Segmentation of moving objects is an important step toward monitoring and activity analysis. This paper proposes a real-time moving object segmentation technique using Bayer-pattern images. The proposed method takes care of low resource availability at embedded system. In order to reduce memory requirements, we model the background scene in single channel Bayer-pattern domain. In classification phase, we first define an approximate foreground using block-level information. The Approximate foreground is then refined using in-loop interpolated pixel-level RGB information. Refinement process also takes care of illumination changes. Our proposed method achieves almost the same accuracy as state-of-the-art RGB based pixel-level background subtraction methods, while using lower computational resources. Experimental results show that the proposed has a true positive rate of 87% and false positive rate 1.8% using low resources, quite suitable for implementation in real-time embedded systems that can be used for monitoring.