Stefan Hegemann

Real-Time Stereo Vision: Making More Out of Dynamic Programming

Dynamic Programming (DP) is a popular and efficient method for calculating disparity maps from stereo images. It allows for meeting real-time constraints even on low-cost hardware. Therefore, it is frequently used in real-world applications, although more accurate algorithms exist. We present a refined DP stereo processing algorithm which is based on a standard implementation. However it is more flexible and shows increased performance. In particular, we introduce the idea of multi-path backtracking to exploit the information gained from DP more effectively. We show how to automatically tune all parameters of our approach offline by an evolutionary algorithm. The performance was assessed on benchmark data. The number of incorrect disparities was reduced by 40 % compared to the DP reference implementation while the overall complexity increased only slightly.

Realtime Roadboundary Detection for Urban Areas

In the context of driver assistance systems lane detection systems are used for lane departure warning and lane centering functions. In some cases, especially in urban scenarios, no lane marks are available and for some circumstances lane marks are not sufficient to assist the driver. For novel urban functions it is necessary to know in which areas the car can operate. This problem requires detecting the free space as a traversable area for vehicles and has to take into account small objects such as curbstones. In this paper we present a real-time road boundary detection for urban areas to realize an easy lane recognition algorithm by using a series hardware stereo camera system. The algorithm is based on dense stereo information.