Abstract
This chapter presents a high speed, low latency stereo vision based collision warning system for automotive applications. The system uses two high speed cameras running at 100 fps and achieves latency below 0.1s by using an Nvidia Tesla C1060 GPU for accelerating computational expensive algorithms. From each pair of captured stereo images a disparity map is computed using the block matching algorithm, which is afterwards segmented in order to detect different objects in the scene. This segmentation is performed using a novel segmentation method based on the pixels’ intensity value and their connectivity. For each detected object its distance to the front of the vehicle is computed and the degree of danger is estimated by the collision warning module. Extensive experiments show that the presented system delivers reliable results for object detection as well as precise results in terms of estimated distance to the detected objects.
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Leu, A., Aiteanu, D., Gräser, A. (2012). High Speed Stereo Vision Based Automotive Collision Warning System. In: Precup, RE., Kovács, S., Preitl, S., Petriu, E. (eds) Applied Computational Intelligence in Engineering and Information Technology. Topics in Intelligent Engineering and Informatics, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28305-5_15
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DOI: https://doi.org/10.1007/978-3-642-28305-5_15
Publisher Name: Springer, Berlin, Heidelberg
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