An integrated approach to feature based dynamic vision

A novel method for dynamic scene analysis by computer vision is described that combines 3-D shape models, dynamical models as known from modern control theory and the laws of perspective projection. To arrive at numerically efficient real-time algorithms, the recursive state estimation by Kalman filtering is adapted to a feature-based image sequence analysis scheme. The spatial and temporal constraint propagation using an integral spatiotemporal model yields image evaluation cycle times of about 0.1 s for simple but realistic tasks with microprocessors available today. Motion control in the dynamic range of humans is thereby possible. Applications discussed are: three-degree-of-freedom planar docking, road vehicle guidance at speeds up to 60 mph and six-degree-of-freedom landing approach of a business jet plane (hardware in the loop simulation)