One of the most important application on Intelligent Transporting System (ITS) is to analyze various traffic activities and construct traffic monitoring system. However, such analyses in previous works have been done by manual inspection to huge amount of traffic images. The major reason why automated analyses of traffic images have been failed is that there does not exist any robust tracking algorithms against such crowded situations at intersections. In order to resolve such a problem, we have developed the tracking algorithm based on Spatio-Temporal Markov Random Field model which is robust against occlusion and clutter problems. Since the algorithm is able to tracking each individual vehicle even in cases of heavy occlusion situations in crowded traffic, this enables to automatically acquire detailed information from traffic images. Therefore utilizing this tracking algorithm, we then constructed an automated system which is able to acquire traffic event statistics such as vehicle counts distinguishing travel directions, velocities, frequent paths and so on. Besides, this system integrates such various statistical Information in order to display correlations among different statistics clearly.