Authors:
Ryogo Takemoto
1
;
Yuya Nagamine
1
;
Kazuki Yoshihiro
1
;
Masatoshi Shibata
2
;
Hideo Yamada
2
;
Yuichiro Tanaka
3
;
Shuichi Enokida
4
and
Hakaru Tamukoh
1
;
3
Affiliations:
1
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0196, Japan
;
2
AISIN CORPORATION, 2-1 Asahi-machi, Kariya, Aichi, 448-8650, Japan
;
3
Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0196, Japan
;
4
Department of Artificial Intelligence, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
Keyword(s):
Image Processing, Human Detection, HOG, MRCoHOG, GMM-MRCoHOG, FPGA.
Abstract:
In this research, we focus on Gaussian mixture model-multiresolution co-occurrence histograms of oriented gradients (GMM-MRCoHOG) features using luminance gradients in images and propose a hardware-oriented algorithm of GMM-MRCoHOG to implement it on a field programmable gate array (FPGA). The proposed method simplifies the calculation of luminance gradients, which is a high-cost operation in the conventional algorithm, by using lookup tables to reduce the circuit size. We also designed a human-detection digital architecture of the proposed algorithm for FPGA implementation using high-level synthesis. The verification results showed that the processing speed of the proposed architecture was approximately 123 times faster than that of the FPGA implementation of VGG-16.