KEYWORDS: Information fusion, Video, Machine learning, Education and training, Network architectures, Action recognition, Convolution, Video surveillance, Data modeling, RGB color model
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".
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