Performance of optical flow techniques
International Journal of Computer Vision
Type-2 fuzzy Gaussian mixture models
Pattern Recognition
Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Fusing color and texture features for background model
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
Neural Network Approach to Background Modeling for Video Object Segmentation
IEEE Transactions on Neural Networks
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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.