Spatio-Temporal MRF model and its Application to Traffic Flow Analyses

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 in 2000. This algorithm is then improved to deal with the problem of illumination variation which is the other dif- ficult problem in computer vision technology. Utilizing this tracking algorithm, an application to acquire traffic flow statistics based on operation hierarchy. This system is able to acquire traffic event statistics such as vehicle counts distinguishing travel directions, velocities, frequent paths and so on.