The aim of the research described in this article is to accelerate object detection in images and video sequences using graphics processors. It includes algorithmic modifications and adjustments of existing detectors, constructing variants of efficient implementations and evaluation comparing with efficient implementations on the CPUs. This article focuses on detection by statistical classifiers based on boosting. The implementation and the necessary algorithmic alterations are described, followed by experimental measurements of the created object detector and discussion of the results. The final solution outperforms the reference efficient CPU/SSE implementation, by approximately 6-89 for high-resolution videos using nVidia GeForce 9800GTX and Intel Core2 Duo E8200.
Hough transform is a well-known and popular algorithm for detecting lines in raster images. The standard Hough transform is rather slow to be usable in real-time, so different accelerated and approximated algorithms exist. This paper proposes a modified accumulation scheme for the Hough transform, which makes it suitable for computer systems with small but fast read-write memory -such as the today's GPUs. The proposed algorithm is evaluated both on synthetic binary images and on complex high resolution real-world photos. The results show that using today's commodity graphics chips, the Hough transform can be computed at interactive frame rates even with a high resolution of the Hough space and with the Hough transform fully computed.
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