A novel background subtraction approach based on multi layered self-organizing maps

State-of-the-art background subtraction approaches exhibit impressive results in several applications, however, they usually employ static parameter values that reduce their flexibility when used in real world environments. In this paper we relax this problem by proposing a novel approach based on temporal analysis for automatic adjustment of the parameter that controls the motion segmentation quality. Moreover, we extend and strengthen the Self Organizing map Background Subtraction (SOBS) into a multi-layered framework defining a multi-modal background color representation. According to the winner-take-all rule, each layer builds a background model representing an independent view of the scene as response to external conditions such as illumination variations, camouflages, and moving backgrounds. Experimental results on the 2012 CVPR Change Detection dataset, demonstrate that our approach outperforms SOBS achieving promising results compared with those obtained by state-of-the-art background subtraction methodologies.