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http://dx.doi.org/10.5909/JBE.2015.20.3.421

Implementation of Pedestrian Detection and Tracking with GPU at Night-time  

Choi, Beom-Joon (Dept. of Electronic Eng., Kyungsung University)
Yoon, Byung-Woo (Dept. of Electronic Eng., Kyungsung University)
Song, Jong-Kwan (Dept. of Electronic Eng., Kyungsung University)
Park, Jangsik (Dept. of Electronic Eng., Kyungsung University)
Publication Information
Journal of Broadcast Engineering / v.20, no.3, 2015 , pp. 421-429 More about this Journal
Abstract
This paper is about an approach for pedestrian detection and tracking with infrared imagery. We used the CUDA(Computer Unified Device Architecture) that is a parallel processing language in order to improve the speed of video-based pedestrian detection and tracking. The detection phase is performed by Adaboost algorithm based on Haar-like features. Adaboost classifier is trained with datasets generated from infrared images. After detecting the pedestrian with the Adaboost classifier, we proposed a particle filter tracking strategies on HSV histogram feature that exploit adaptively at the same time. The proposed approach is implemented on an NVIDIA Jetson TK1 developer board that is full-featured device ideal for software development within the Linux environment. In this paper, we presented the results of parallel processing with the NVIDIA GPU on the CUDA development environment for detection and tracking of pedestrians. We compared the object detection and tracking processing time for night-time images on both GPU and CPU. The result showed that the detection and tracking speed of the pedestrian with GPU is approximately 6 times faster than that for CPU.
Keywords
Pedestrian Detection; Tracking; Adaboost Algorithm; Particle Filter; GPU;
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  • Reference
1 E. Osuna, R.Freund and F. Girosi, “Training Support Vector Machines: An Application to Face Detection,“ Proc. on IEEE Conf. Computer Vision and Pattern Recognition, pp. 130-136, 1997.
2 D. Geronimo, A. M. Lopez, A. D. Sappa and T. Graf, "Survey of Pedestrian Detection for Advanced Driver Assistance Systems," IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 7, pp.1239-1258, July, 2010.   DOI   ScienceOn
3 D. Xia, H. Sun and Z. Shen, "Real-time Infrared Pedestrian Detection Based on Multi-block LBP," Proc. on 2010 International Conference on Computer Application and System Modeling, vol. 12, pp. 140-142, 2010.
4 M. Bertozzi, A. Broggi, C. Caraffi, M. Del Rose, M. Felisa and G. Vezzoni, “Pedestrian Detection by Means of Far-infrared Stereo Vision,” Computer vision and image understanding 106, pp. 194-204, 2007.   DOI   ScienceOn
5 P. Viola and M. Jones, “Robust Real Time Object Detection,“ Proc. on IEEE ICCV Workshop on Statistical and Computer Theories of Vision, 2001.
6 J. Giebel, D. Gavrila, and C. Schnorr, "A Bayesian Framework for Multi-Cue 3D Object Tracking," Proc. on European Conf. Computer Vision, pp. 241-252, 2004.
7 U. Franke and A. Joos, "Real-Time Stereo Vision for Urban Traffic Scene Understanding," Proc. on IEEE Intelligent Vehicles Symp, pp. 273-278, 2000.
8 I. S. Kim and H. Shin, "A Study on Developmrnt od Intelligent CCTV Security System Basrd on BIM," Journal of the Korea Institute of Electronic Communication Sciences, vol. 6, no. 5, pp. 789-795, 2011.
9 M. Isard and A. Blake, “CONDENSATION–Conditional Density Propagation for Visual Tracking,” International Journal on Computer Vision vol. 29, no. 1, pp. 5-28, 1998.   DOI
10 K. Nummiaro, E. Koller-Meier, and L. V. Gool, “A Color-based Particle Filter,” Proc. of 1st International workshop on generative-model-based vision, pp. 53-60, 2002.
11 http://en.wikipedia.org/wiki/Graphics_processing_unit.
12 https://developer.nvidia.com/cuda-zone
13 NVIDIA CUDA "Cuda Reference Manual v2.0"
14 NVIDIA CUDA "CUDA C Best Practices Guide v6.5"
15 NVIDIA CUDA C Programming Guide, Version 4.0
16 http://docs.opencv.org/modules/gpu/doc/gpu.html