Memory-Efficient Implementation of GMM-MRCoHOG for Human Recognition Hardware

Ryogo Takemoto, Yuya Nagamine, Kazuki Yoshihiro, Masatoshi Shibata, Hideo Yamada, Yuichiro Tanaka, Shuichi Enokida, Hakaru Tamukoh, Hakaru Tamukoh

2023

Abstract

High-speed and accurate human recognition is necessary to realize safe autonomous mobile robots. Recently, human recognition methods based on deep learning have been studied extensively. However, these methods consume large amounts of power. Therefore, this study focuses on the Gaussian mixture model of multiresolution co-occurrence histograms of oriented gradients (GMM-MRCoHOG), which is a feature extraction method for human recognition that entails lower computational costs compared to deep learning-based methods, and aims to implement its hardware for high-speed, high-accuracy, and low-power human recognition. A digital hardware implementation method of GMM-MRCoHOG has been proposed. However, the method requires numerous look-up tables (LUTs) to store state spaces of GMM-MRCoHOG, thereby impeding the realization of human recognition systems. This study proposes a LUT reduction method to overcome this drawback by standardizing basis function arrangements of Gaussian mixture distributions in GMM-MRCoHOG. Experimental results show that the proposed method is as accurate as the previous method, and the memory required for state spaces consuming LUTs can be reduced to 1/504th of that required in the previous method.

Paper Citation

in Harvard Style

Takemoto R., Nagamine Y., Yoshihiro K., Shibata M., Yamada H., Tanaka Y., Enokida S. and Tamukoh H. (2023). Memory-Efficient Implementation of GMM-MRCoHOG for Human Recognition Hardware. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 648-655. DOI: 10.5220/0011698400003417

in Bibtex Style

@conference{visapp23,
author={Ryogo Takemoto and Yuya Nagamine and Kazuki Yoshihiro and Masatoshi Shibata and Hideo Yamada and Yuichiro Tanaka and Shuichi Enokida and Hakaru Tamukoh},
title={Memory-Efficient Implementation of GMM-MRCoHOG for Human Recognition Hardware},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={648-655},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011698400003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Memory-Efficient Implementation of GMM-MRCoHOG for Human Recognition Hardware
SN - 978-989-758-634-7
AU - Takemoto R.
AU - Nagamine Y.
AU - Yoshihiro K.
AU - Shibata M.
AU - Yamada H.
AU - Tanaka Y.
AU - Enokida S.
AU - Tamukoh H.
PY - 2023
SP - 648
EP - 655
DO - 10.5220/0011698400003417
PB - SciTePress