Shoe Recognition Model with Floor Pressure Sensors

Abstract概要

In research on traffic flow analysis, computer vision methods using camera images have been the predominant approach. However, using cameras for flow line analysis presents challenges, such as creating blind spots caused by obstructions such as people or objects. Additionally, privacy concerns arise. These issues can be mitigated using floor pressure sensors for flow line analysis. To successfully perform flow line analysis with these sensors, it is essential to identify individuals based on factors such as weight, stride length, speed, and shoe type. In this study, we developed a system to identify shoe types from footprint pressure distribution using a neural network model. Our focus was on three types of shoes: sneakers, room shoes, and sandals. We collected data for each category and created a recognition model, achieving an F-measure of 97.6% in the best model. The primary challenges for practical implementation are measurement time and durability.

Artifacts関連資料

Information書誌情報

掲載誌・会議名

The Eighteenth International Conference on Sensor Technologies and Applications

ページ

10-11

出版日

2024/11/03

発表日

2024/11/06

発表場所

Hotel Novotel Cap 3000 (Nice, France)

ISSN

2308-4405

ISBN

978-1-68558-207-4

Keywordsキーワード

Sora Kamimura, Tetsuo Yutani, Atsuko Shibuya, Tsubasa Yumura. Shoe Recognition Model with Floor Pressure Sensors, The Eighteenth International Conference on Sensor Technologies and Applications, pp.10-11, 2024

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