Vehicle Tracking in Low-angle and Front-View Images based on Spatio-Temporal Markov Random Field Model

One of the most important research in ITS is the development of systems that automatically analyze or monitor traffic activities. For that purpose, it is necessary to achieve reliable vehicle tracking in traffic images. However, occlusion effect among vehicles had impeded such reliable tracking for a long time. In order to solve this problem we have developed the dedicated tracking algorithm, referred to as Spatio-Temporal Markov Random Field in the year of 2000. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes. And it was proved that the algorithm has performed 95% success of tracking in middle-angle image. However, most of images actually captured by cameras on infrastructures are lowangle image, and many of them are front-view image. Since vehicles severely occlude each other in such images, segmentations of vehicle region through spatio-temporal images will be unsuccessful. In order to resolve such a problem, we have improved this ST-MRF model to re-optimize segmentation boundaries through accumulated spatio-temporal images. As a result, the improved algorithm were able to track vehicles at 91.2% success rate against such sever occlusions in low-angle and front view images at a highway junction. This successful result would lead precise analyses of severely complicated traffics.