Elevator door motion recognition utilizing an imitation C3D network

Yuhu Nie; Zhe Zheng; Yuntao Huang; Xiangfeng Gong

doi:10.1117/12.3012559

21 December 2023 Elevator door motion recognition utilizing an imitation C3D network

Yuhu Nie, Zhe Zheng, Yuntao Huang, Xiangfeng Gong

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129700H (2023) https://doi.org/10.1117/12.3012559
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

This study focuses on the application of elevator door motion recognition, comparing the capability of 2D Convolutional Neural Networks (2D CNNs) and 3D Convolutional Neural Networks (3D CNNs) in extracting spatio-temporal information, further exploring the factors contributing to their differences. The inquiry stems from observations regarding the significant computational power demanded by 3D CNNs during embedded deployment, while the proposed improvements arise from multi-modal information fusion concepts. Through experimental validation, we establish the efficiency of 2D CNNs in motion recognition tasks, employing the computational simplicity of 2D CNNs to match the precision of 3D CNNs. This strategy leads to the introduction of a novel 2D CNN architecture, termed as the "Imitation C3D Network".

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuhu Nie, Zhe Zheng, Yuntao Huang, and Xiangfeng Gong "Elevator door motion recognition utilizing an imitation C3D network", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129700H (21 December 2023); https://doi.org/10.1117/12.3012559

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KEYWORDS

Information fusion

Video

Machine learning

Education and training

Action recognition

Network architectures

Convolution

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