| アイテムタイプ |
会議発表論文 = Conference Paper(1) |
| 公開日 |
2024-04-16 |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
|
資源タイプ |
conference paper |
| タイトル |
|
|
タイトル |
Memory-Efficient Implementation of GMM-MRCoHOG for Human Recognition Hardware |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 著者 |
Takemoto, Ryogo
Nagamine, Yuya
Yoshihiro, Kazuki
Shibata, Masatoshi
Yamada, Hideo
田中, 悠一朗
榎田, 修一
田向, 権
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
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 distrib utions 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. |
|
言語 |
en |
| 備考 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP: VISAPP, February 19-21, 2023, Lisbon, Portugal |
|
言語 |
en |
| 書誌情報 |
en : Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP
p. 648-655,
発行日 2023
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| 出版社 |
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|
出版社 |
ScitePress |
|
言語 |
en |
| DOI |
|
|
関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.5220/0011698400003417 |
| ISBN |
|
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|
識別子タイプ |
ISBN |
|
|
関連識別子 |
978-989-758-634-7 |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2184-4321 |
| 著作権関連情報 |
|
|
権利情報 |
Copyright (c) 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0) |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Image Processing |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Human Recognition |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Human Detection |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
HOG |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
MRCoHOG |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
GMM-MRCoHOG |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
FPGA |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 査読の有無 |
|
|
値 |
yes |
| 研究者情報 |
|
|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/100000641_ja.html |
| 論文ID（連携） |
|
|
値 |
10406431 |
| 連携ID |
|
|
値 |
12092 |
10406431.pdf (8.8 MB)
