Multivalued Background/Foreground Separation for Moving Object Detection

The detection of moving objects is usually approached by background subtraction, i.e. by constructing and maintaining an up-to-date model of the background and detecting moving objects as those that deviate from such a model. We adopt a previously proposed approach to background subtraction based on self organization through artificial neural networks, that has been shown to well cope with several of the well known issues for background maintenance, featuring high detection accuracy for different types of videos taken with stationary cameras. Here we formulate a fuzzy approach to the background model update procedure to deal with decision problems typically arising when crisp settings are involved. We show through experimental results that higher accuracy values can be reached for color video sequences that represent typical situations critical for moving object detection.