Occlusion robust tracking utilizing spatio-temporal Markov random field model

It is very important to achieve reliable vehicle tracking in ITS application such as accident detection. The most difficult problem associated with vehicle tracking is the occlusion effect among vehicles. In order to resolve this problem, we applied the dedicated algorithm which we defined as spatio-temporal Markov random field model to traffic images at an intersection. The spatio-temporal MRF considers texture correlations between consecutive images as well as the correlation among neighbors within a image. As a result, we were able to track vehicles at the intersection robustly against occlusions. Vehicles appear in various kinds of shapes and they move in random manners at the intersection. Although occlusions occur in such complicated manners, the algorithm given was able to segment and track such occluded vehicles at a high success rate of 93-96%. The algorithm requires only gray scale images and does not assume any physical models of vehicles.