Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/113022
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Type: Conference paper
Title: TenniSet: A dataset for dense fine-grained event recognition, localisation and description
Author: Faulkner, H.
Dick, A.
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
Publisher: IEEE
Publisher Place: Piscataway, NJ
Issue Date: 2017
ISBN: 1538628406
9781538628409
Conference Name: International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017) (29 Nov 2017 - 01 Dec 2017 : Sydney, AUSTRALIA)
Statement of
Responsibility: 
Hayden Faulkner, Anthony Dick
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.
Rights: ©2017 IEEE
RMID: 0030086328
DOI: 10.1109/DICTA.2017.8227494
Published version: https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8226656
Appears in Collections:Computer Science publications

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