3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 71 references that are in the map. First posted on arXiv 13 Apr 2022.
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23 of 71 references.
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11 more
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