Bias-corrected optical flow estimation for road vehicle tracking

Model-based vehicle tracking in traffic image sequences can be made more robust by matching expected displacement rates of vehicle surface points to optical flow (OF) vectors computed from an image sequence. The capability to track vehicles uninterruptedly in this manner over extended image sequences results in the ability to investigate even small errors in OF estimation. It turns out that the OF magnitudes are systematically underestimated. The-albeit small-bias can be corrected by analyzing the influence of explicitly modeled grey value noise on the precision of OF values estimated by means of the neighborhood sampling method.