Traffic Sign Distance Estimation Based on Stereo Vision and GPUs
Recognition, detection, and distance determination of traffic signs have become essential tasks for the development of Intelligent Transport Systems (ITS). Processing time is very important to these tasks, since they not only need an accurate answer, but also require a real-time response. The distance determination of traffic Signs (TS) uses the greatest number of computational resources for the disparity map calculations based on the Stereo Vision method. In this paper, we propose the acceleration of the disparity map calculation by using our parallel algorithm, called Accelerated Estimation for Traffic Sign Distance (AETSD) and implemented in the Graphics Processors Unit (GPU), which uses data storage strategies based on their frequency of use. Furthermore, it carries out an optimized search for the traffic signal in the stereoscopic pair of images. The algorithm splits the problem into parts and they are solved concurrently by the available massive processors into the stream processors units (SM). Our results show that the proposed algorithm accelerated the response time 141 times for an image resolution of 1024 × 680 pixels, with an execution time 0.04 s for the AETSD parallel version and 5.67 s for the sequential version.
KeywordsTraffic sign Stereo vision Parallel processing
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