TenniSet: A dataset for dense fine-grained event recognition, localisation and description
Date
2017
Authors
Faulkner, H.
Dick, A.
Editors
Guo, Y.
Li, H.
Cai, W.
Murshed, M.
Wang, Z.
Gao, J.
Feng, D.
Li, H.
Cai, W.
Murshed, M.
Wang, Z.
Gao, J.
Feng, D.
Advisors
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Conference paper
Citation
Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017), 2017 / Guo, Y., Li, H., Cai, W., Murshed, M., Wang, Z., Gao, J., Feng, D. (ed./s), vol.2017-December, pp.1-8
Statement of Responsibility
Hayden Faulkner, Anthony Dick
Conference Name
International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017) (29 Nov 2017 - 1 Dec 2017 : Sydney, AUSTRALIA)
Abstract
This paper introduces a new video understanding dataset which can be utilised for the related problems of event recognition, localisation and description in video. Our dataset consists of dense, well structured event annotations in untrimmed video of tennis matches. We also include highly detailed commentary style descriptions, which are heavily dependent on both the occurrence as well as the sequence of particular events. We use general deep learning techniques to acquire some initial baseline results on our dataset, without the need for explicit domain-specific assumptions.
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©2017 IEEE