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http://dx.doi.org/10.13067/JKIECS.2015.10.2.239

Comparison Speed of Pedestrian Detection with Parallel Processing Graphic Processor and General Purpose Processor  

Park, Jang-Sik (경성대학교 전자공학과)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.10, no.2, 2015 , pp. 239-246 More about this Journal
Abstract
Video based object detection is basic technology of implementing smart CCTV system. Various features and algorithms are developed to detect object, however computations of them increase with the performance. In this paper, performances of object detection algorithms with GPU and CPU are compared. Adaboost and SVM algorithm which are widely used to detect pedestrian detection are implemented with CPU and GPU, and speeds of detection processing are compared for the same video. As results of frame rate comparison of Adaboost and SVM algorithm, it is shown that the frame rate with GPU is faster than CPU.
Keywords
Object Detection; Adaboost; SVM; Parallel processing; GPU;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 H-M. Moon and S-B. Pan, "The human identification method in video surveillance system," J. of The Korean Institute of Information Technology, vol. 8, no. 5, May 2010, pp. 199-206.
2 H-M. Moon and S-B. Pan, "The analysis of de-identification for privacy protection in intelligent video surveillance system," J. of The Korean Institute of Information Technology, vol. 9, no. 7, July 2011, pp. 189-200.
3 H.-T. Kim, G.-H. Lee, J.-S. Park, and Y.-S. Yu, "Vehicle detection in tunnel using Gaussian mixture model and mathematical morphological processing," J. of The Korean Institute of Electronic Communication Sciences, vol. 7, no. 5, Oct. 2012, pp. 967-974.   과학기술학회마을
4 M.-W. Kim, C.-M. Oh, D. Aurrahman, Y.-G. Ahn, and C.-W. Lee, "The virture screen using skin tone and GMM foreground segmentation," In Proc. Conf. of The Korea Information Processing Society, vol. 15, no. 1, May 2008, pp. 179-181.
5 C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models for real-time tracking," In Proc. IEEE Computer Vision and Pattern Recognition(CVPR) 1999, vol. 2, June 1999, pp. 246-252.
6 A. Elgammal, D. Harwood, and L. S. Davis, "Non-parametric model for background subtraction," In Proc. European Conf. on Computer Vision(ECCV 2000), vol. 1843, June. 2000, pp. 751-767.
7 T. Ahonen, A. Hadid and M. Pietikaninen, "Face description with local binary patterns : application to face recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, Dec. 2006. pp. 2037-2041.   DOI   ScienceOn
8 D. Geronimo, A. D. Sappa, A. Lopez and D. Ponsa, "Pedestrian detection using Adaboost learning of features and vehicle pitch estimation," In Proc. Int. Conf. Visualization, Imaging and Image Processing(IASTED), Palma de Mallorca, Spain, Aug. 2006. pp. 400-405.
9 Thorsten Joachims, "Training linear SVMs in linear time," In Proc. of Int. conf. on Knowledge Discovery and Data Mining(KDD), Philadelphia Pennsylvania, Aug. 2006. pp. 217-226.
10 "NVIDIA Tegra K1, A New Era in Mobile Computing," White-paper of NVIDIA, 2013.