HOG-AdaBoost Implementation for Human Detection Employing FPGA ALTERA DE2-115

Abstract

Human detection system using Histogram of Oriented Gradients (HOG) feature and AdaBoost classifier (HOG-AdaBoost) in FPGA ALTERA DE2-115 are presented in this paper. This work is expanded version from our previous study. This paper discusses 1) the HOG performance in detecting human from a passive images with other point-of-views (30 deg., 40 deg., 50 deg., 60 deg. and up to 70 deg.); 2) FPS test with various image sizes (320 x 240, 640 x 480, 800 x 600, and 1280 x 1024); 3) re-measurement the FPGA’s power consumption and 4) simulate the architecture in RTL. We used three databases as a parameter for test purpose, i.e. INRIA, MIT, and Daimler.

Authors and Affiliations

Trio Adiono Adiono, Kevin Shidqi Prakoso, Christoporus Deo Putratama, Bramantio Yuwono, Syifaul Fuada

Keywords

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  • EP ID EP408100
  • DOI 10.14569/IJACSA.2018.091042
  • Views 86
  • Downloads 0

How To Cite

Trio Adiono Adiono, Kevin Shidqi Prakoso, Christoporus Deo Putratama, Bramantio Yuwono, Syifaul Fuada (2018). HOG-AdaBoost Implementation for Human Detection Employing FPGA ALTERA DE2-115. International Journal of Advanced Computer Science & Applications, 9(10), 353-358. https://europub.co.uk/articles/-A-408100