  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.
