THETIS: Three Dimensional Tennis Shots a Human Action Dataset

Sofia Gourgari, Georgios Goudelis, Konstantinos Karpouzis, Stefanos Kollias; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2013, pp. 676-681


The detection and classification of human movements, as a joint field of Computer Vision and Pattern Recognition, is used with an increasing rate in applications designed to describe human activity. Such applications require efficient methods and tools for the automatic analysis and classification of motion capture data, which constitute an active field of research. To facilitate the development and the benchmarking of methods for action recognition, several video collections have previously been proposed. In this paper, we present a new video database that can be used for an objective comparison and evaluation of different motion analysis and classification methods. The database contains video clips that capture the 3D motion of individuals. To be more specific, the set consists of 8374 video clips, which contain 12 different types of tennis actions performed by 55 individuals, captured by Kinect. Kinect provides the depth map of motion data and helps to extract the 3D skeletal joint connections. Performing experiments using state of the art algorithms, the database shows to be very challenging. It contains very similar to each other actions, offering the opportunity to algorithms dedicated to gaming and athletics, to be developed and tested. The database is freely available for research purposes.

Related Material

author = {Gourgari, Sofia and Goudelis, Georgios and Karpouzis, Konstantinos and Kollias, Stefanos},
title = {THETIS: Three Dimensional Tennis Shots a Human Action Dataset},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2013}