Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/113022
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
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 - 1 Dec 2017 : Sydney, AUSTRALIA) |
Editor: | Guo, Y. Li, H. Cai, W. Murshed, M. Wang, Z. Gao, J. Feng, D. |
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 |
DOI: | 10.1109/DICTA.2017.8227494 |
Published version: | https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8226656 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.