Enokida,  Shuichi

Nagamine,  Yuya

Shibata,  Masatoshi

Takemoto,  Ryogo

Tamukoh, Hakaru

Tanaka, Yuichiro

Yamada,  Hideo

Yoshihiro,  Kazuki

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-16.17th International Joint Conference on Computer Vision Theory and Applications (VISAPP 2022), February 6-8, 2022, Online Streamin

田中, 悠一朗

タナカ, ユウイチロウ

田向, 権

タムコウ, ハカル

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Hardware-Oriented Algorithm for Human Detection using GMM-MRCoHOG Features

Hardware-Oriented Algorithm for Human Detection using GMM-MRCoHOG Features

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