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arXiv · 2204.08460

3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition

Pierre-Etienne Martin·J Benois-Pineau·R Péteri·A Zemmari·J Morlier
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Abstract

3D convolutional networks is a good means to perform tasks such as video segmentation into coherent spatio-temporal chunks and classification of them with regard to a target taxonomy. In the chapter we are interested in the classification of continuous video takes with repeatable actions, such as strokes of table tennis. Filmed in a free marker less ecological environment, these videos represent a challenge from both segmentation and classification point of view. The 3D convnets are an efficient tool for solving these problems with window-based approaches.

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Pierre-Etienne Martin, J Benois-Pineau, R Péteri, A Zemmari, J Morlier. 2022-04-13. 3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition. https://arxiv.org/abs/2204.08460

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