Segmentation of vehicles and pedestrians in traffic scene by spatio-temporal Markov random field model

For a long period, object tracking in images has suffered from occlusion problems. In order to resolve occlusion problems, we proposed the spatio-temporal Markov random field model for segmentation of spatio-temporal images (Kamijo, S. et al., ICPR´00, vol.1, p.142-7, 2000). This S-T MRF optimizes the segmentation boundaries of occluded objects and their motion vectors simultaneously, by referring to textures and segment labeling correlations along the temporal axis, as well as the spatial axes. As a result, tracking moving objects became very successful against occlusion. Since then, the S-T MRF model has been practically applied to vehicle tracking to reveal good results against occlusion. However, the S-T MRF model was defined to be a general model for the segmentation of spatio-temporal images, and the model is independent of the shape models of target objects. Therefore, in addition to solid objects such as vehicles, the model would be effective for tracking flexible objects, such as pedestrians, against occlusion and clutter situations. We prove the S-T MRF model to be effective for segmentation of traffic scenes which are cluttered by vehicles and pedestrians.