  We propose a traffic congestion estimation system based on unsupervised
on-line learning algorithm. The system does not rely on background extraction
or motion detection. It extracts local features inside detection regions of
variable size which are drawn on lanes in advance. The extracted features are
then clustered into two classes using K-means and Gaussian Mixture Models(GMM).
A Bayes classifier is used to detect vehicles according to the previous cluster
information which keeps updated whenever system is running by on-line EM
algorithm. Experimental result shows that our system can be adapted to various
traffic scenes for estimating traffic status.
