Fast Pedestrian Detection Based on GPU+CPU

Heng Li Liu; Si Yue Zhou; Zheng Peng Yuan; Yu Chi Wang

doi:10.4028/www.scientific.net/AMR.718-720.2291

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 718-720Fast Pedestrian Detection Based on GPU+CPU

Fast Pedestrian Detection Based on GPU+CPU

Abstract:

Accurate pedestrian detection is required for practical applications such as automotive and security applications. However, the implementation does not have enough performance because the present schemes are not sufficient. In this paper, the authors proposed parallel implementation of HOG-based pedestrian detection on GPU to obtain real-time processing results. By the proposed implementation, the total processing speed becomes 60 times faster than that of original one on frame rate and real-time processing is achieved.

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Periodical:

Advanced Materials Research (Volumes 718-720)

Pages:

2291-2295

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.718-720.2291

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Online since:

July 2013

Authors:

Heng Li Liu, Si Yue Zhou, Zheng Peng Yuan, Yu Chi Wang

Keywords:

Graphic Processing Unit (GPU), Histograms of Oriented Gradient (HOG), Parallel Implementation, Pedestrian Detection, Real-Time

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References

[1] D. Lowe, "Distinctive image features from scale-invariant keypoints," Int'l J. Computer Vision, vol. 60, no. 2, p.91–110, 2004.

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar

[2] N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. of CVPR, vol. 1, 2005, p.886–893.

Google Scholar

[3] P. Sabzmeydani and G. Mori, "Detecting pedestrians by learning shapelet features," in Proc. CVPR, 2007.

DOI: 10.1109/cvpr.2007.383134

Google Scholar

[4] P. Viola, M. J. Jones, and D. Snow, "Detecting pedestrians using patterns of motion and appearance," Int'l J. Computer Vision, vol. 63, p.153–161, 2005.

DOI: 10.1007/s11263-005-6644-8

Google Scholar

[5] E. Shechtman and M. Irani, "Matching local self-similarities across images and videos," in Proc. CVPR, 2007.

DOI: 10.1109/cvpr.2007.383198

Google Scholar

[6] M. Leordeanu, M. Hebert, and R. Sukthankar, "Beyond local appearance: Category recognition from pairwise interactions of simple features," in Proc. CVPR, 2007.

DOI: 10.1109/cvpr.2007.383091

Google Scholar

[7] O. Tuzel, F. Porikli, and P. Meer, "Pedestrian detection via classification on Riemannian manifolds," vol. 30, no. 10, p.1713–1727, 2008.

DOI: 10.1109/tpami.2008.75

Google Scholar

[8] S. Maji, A. Berg, and J. Malik, "Classification using intersection kernel support vector machines is efficient," in Proc. CVPR, 2008.

DOI: 10.1109/cvpr.2008.4587630

Google Scholar

[9] Qiang Zhu, Shai Avidan, Mei C. Yeh, and Kwang T. Cheng. Fast human detection using a cascade of histograms of oriented gradients. In CVPR, pages 1491–1498. IEEE Computer Society, 2006.

DOI: 10.1109/cvpr.2006.119

Google Scholar

[10] NVIDIA Corporation．CUDA C Programming Guide 4.0. 2011, 6

Google Scholar