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Convolutional Neural Networks for Multivariate Time Series Classification using both Inter- and Intra- Channel Parallel Convolutions

Abstract : In this paper, we study a convolutional neural network we recently introduced in [9], intended to recognize 3D hand gestures via multivariate time series classification. The Convolutional Neural Network (CNN) we proposed processes sequences of hand-skeletal joints' positions using parallel convolutions. We justify the model's architecture and investigate its performance on hand gesture sequence classification tasks. Our model only uses hand-skeletal data and no depth image. Experimental results show that our approach achieves a state-of-the-art performance on a challenging dataset (DHG dataset from the SHREC 2017 3D Shape Retrieval Contest).Our model achieves a 91.28% classification accuracy for the 14 gesture classes case and an 84.35% classification accuracy for the 28 gesture classes case.
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Contributor : Fabien Moutarde <>
Submitted on : Friday, October 5, 2018 - 1:53:42 PM
Last modification on : Thursday, September 24, 2020 - 5:04:02 PM
Long-term archiving on: : Sunday, January 6, 2019 - 3:49:33 PM

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  • HAL Id : hal-01888862, version 1

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Guillaume Devineau, Wang Xi, Fabien Moutarde, Jie Yang. Convolutional Neural Networks for Multivariate Time Series Classification using both Inter- and Intra- Channel Parallel Convolutions. Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP'2018), Jun 2018, Marne la Vallée, France. ⟨hal-01888862⟩

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