Vehicle-type identification through automated virtual loop assignment and block-based direction-biased motion estimation

This paper presents a method of automated virtual loop assignment and direction-based motion estimation. The unique features of our approach are that: 1) a number of loops are automatically assigned to each lane. The merit of doing this is that it accommodates pan-tilt-zoom actions without needing further human interaction; 2) the size of the virtual loops is much smaller for estimation accuracy; and 3) the number of virtual loops per lane is large. The motion content of each block may be weighted and the collective result offers a more reliable and robust approach in motion estimation. Comparing this with traditional inductive loop detectors, there are a number of advantages. Our simulation results indicate that the proposed method is effective in type classification.