A methodology for resolving severely occluded vehicles based on component-based multi-resolution relational graph matching

This paper presents a method for resolving severely occluded vehicles (SOV) frequently appear in images of congested traffic. The proposed method is based on the concept of modeling vehicle components graphically in an object hierarchy. By extracting component description of a vehicle, constructing a representative partial graph and matching it with the vehicle graph model defined a priori, the missing components due to visual occlusion can be identified. Experimental results have shown that the proposed method can partition the clustered graph of SOVs in image that are located far away from the camera as well as identifying the missing components of the vehicles. Moreover, it can classify the vehicle type based on the missing components as well as the vehicle graph model.