Foreground detection by robust PCA solved via a linearized alternating direction method
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
A new framework for background subtraction using multiple cues
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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The detection of moving objects from stationary cameras 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. Here, we propose a spatial coherence variant to such approach to enhance robustness against false detections and formulate a fuzzy model to deal with decision problems typically arising when crisp settings are involved. We show through experimental results and comparisons that higher accuracy values can be reached for color video sequences that represent typical situations critical for moving object detection.