Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Robust abandoned object detection using dual foregrounds
EURASIP Journal on Advances in Signal Processing
Linear-projection-based classification of human postures in time-of-flight data
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Integrating vision and language: semantic description of traffic events from image sequences
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
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We propose a Bayesian learning method to capture the background statistics of a dynamic scene. We model each pixel as a set of layered normal distributions that compete with each other. Using a recursive Bayesian learning mechanism, we estimate not only the mean and variance but also the probability distribution of the mean and covariance of each model. This learning algorithm preserves the multimodality of the background process and is capable of estimating the number of required layers to represent each pixel.