Hierarchical Bayer-pattern based background subtraction for low resource devices
Muhammad Ali Shoaib, Tobias Elbrandt, Evgeny Zaretskiy, Jörn Östermann · 2012
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.