Motion detection in video streams is a challenging task for several computer vision applications. Indeed, segmentation of moving and static elements in the scene allows to increase the efficiency of several challenging tasks, such as human-computer interface, robot visions, and intelligent surveillance systems. In this paper, we approach motion detection through a multi-layered artificial neural network, which is able to build for each background pixel a multi-modal color distribution evolving over time through self-organization. According to the winner-take-all rule, each layer of the network models an independent state of the background scene, in response to external disturbing conditions, such as illumination variations, moving backgrounds, and jittering. As a result, our background subtraction method exhibits high generalization capabilities that in combination with a post-processing filtering schema allow to produce accurate motion segmentation. Moreover, we propose an approach to detect anomalous events (such as camera motion) that require background model re-initialization. We describe our method in full details and we compare it against the most recent background subtraction approaches. Experimental results for video sequences from the 2012 and 2014 CVPR Change Detection data sets demonstrate how our methodology outperforms many state-of-the-art methods in terms of detection rate.

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.