Automatic differentiation facilitates OF-integration into steering-angle-based road vehicle tracking

The interpretation of road traffic scenes recorded by a stationary video camera requires a sufficiently large field of view in order to capture non-trivial maneuvers. Vehicle images will thus be small. Their reliable tracking needs to combine edge-based approaches for precision with area-oriented approaches, for example based on a match between estimated and predicted Optic Flow fields, for stability. The approach reported here estimates the steering angle of vehicles to be tracked and compares this as well as a velocity estimate with actual measurements performed in a suitably equipped test vehicle recorded while crossing a road intersection. In addition, tracking results for numerous other vehicles recorded in the same scene have been assessed interactively and are reported. Computation of the Jacobi matrix for the Kalman-Filter is facilitated by a “dual number” representation for partial derivatives