Hierarchical Bayer-pattern Based Foreground Detection
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 percent and false positive rate
1.8 percent using low resources, quite suitable for implementation in
real-time embedded systems that can be used for monitoring.
Videos Segmentation results
Intelligent Room
Hall Monitor
Hall
Hallway
Campus
Pets2001_1
Laboratory
Seam
HighwayI
Office
Office1
Corridor1
Corridor2
Living Lab