Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A linear-time component-labeling algorithm using contour tracing technique
Computer Vision and Image Understanding
Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
Flexible background mixture models for foreground segmentation
Image and Vision Computing
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
A quasi-Bayes unsupervised learning procedure for priors (Corresp.)
IEEE Transactions on Information Theory
A Multiple-Hypothesis Approach for Multiobject Visual Tracking
IEEE Transactions on Image Processing
Integrated Computer-Aided Engineering
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The unsupervised learning of multivariate mixture models from on-line data streams has attracted the attention of researchers for its usefulness in real-time intelligent learning systems. The EM algorithm is an ideal choice for iteratively obtaining maximum likelihood estimation of parameters in presumable finite mixtures, comparing to some popular numerical methods. However, the original EM is a batch algorithm that works only on fixed datasets. To endow the EM algorithm with the capability to process streaming data, two on-line variants are studied, including Titterington's method and a sufficient statistics-based method. We first prove that the two on-line EM variants are theoretically feasible for training the multivariate normal mixture model by showing that the model belongs to the exponential family. Afterward, the two on-line learning schemes for multivariate normal mixtures are applied to the problems of background learning and moving foreground detection. Experiments show that the two on-line EM variants can efficiently update the parameters of the mixture model and are capable of generating reliable backgrounds for moving foreground detection.