Ein System zur Hindernisdetektion durch Real-Zeit-Auswertung von optischen Fluß-Vektoren

In this contribution we present a system for the detection of stationary obstacles and moving objects in front of a moving vehicle. The detection of obstacles and others objects is based on the evaluation of optical flow vectors calculated in real-time from image sequences. The optical flow vectors are calculated with a local analytical approach. The necessary second order spatio-temporal derivatives are computed in real-time with our second generation custom designed image sequence analysis system MiniVISTA. At all image locations it is tested whether the structure of the gray value distribution is sufficient for a local optical flow calculation or not. Only those sets of spatio-temporal derivatives which passed the structure test are transmitted to subsequent processes being executed on a transputer network. These processes calculate the optical flow vectors, compare the calculated optical flow vectors with the corresponding model vectors, and perform the obstacle test. Experimental results obtained from image sequences recorded on our experimental vehicles MB 609 D and BMW 735 iL are presented

Real-time estimation and tracking of optical flow vectors obstacle. Vectors for obstracle detection

Optical flow contains information about the motion of a camera relative to its environment and about the three-dimensional structure of the imaged scene. In this contribution we use that information to detect obstacles in front of a moving vehicle. Since the detection is based on motion no a-priori knowledge about obstacle shape is required. Optical flow vectors are estimated from spatio-temporal derivatives of the gray value function which are computed at video frame rate by the custom-designed hardware MiniVISTA. To eliminate outliers and to speed up obstacle detection by data reduction the estimated vectors are clustered before they are passed to the obstacle test. The purpose of the obstacle test is to separate moving objects from the stationary environment and to separate elevated objects from the ground plane. In continuation of our previous work, obstacle detection is regarded as a state estimation problem. This enables us to enlarge the motion stereo basis by applying a Kalman filter to track optical flow vectors over subsequent image frames. Experimental results obtained from image sequences recorded with our experimental vehicle are presented

Robust Lane Recognition Embedded in a Real-Time Driver Assistance System

We developed a fast and robust approach for automatic lane detection as part of a real-time driver assistance system. Two different algorithms to extract measurement points are used to detect not only marked but unmarked lane borders as well. Different road types as well as various traffic situations and illumination changes require great care on robustness and reliability. Obstacle information computed by another module in this system helps to increase robustness. The algorithm was extended to track two directly neighboured lanes. Additionally, the distribution of the measurement points is used to classify the marking line types. The system has been integrated into two experimental vehicles and tested with a large data set. It performed very well under different traffic situations and weather conditions