Ninh, Van-Tu 
ORCID: 0000-0003-0641-8806, Le, Tu-Khiem ORCID: 0000-0003-3013-9380, Dang-Nguyen, Duc-Tien ORCID: 0000-0002-2761-2213 and Gurrin, Cathal ORCID: 0000-0003-2903-3968
(2019)
Replay detection and multi-stream synchronization in CS:GO game streams using content-based Image retrieval and Image signature matching.
In: The MediaEval 2019, 10th Anniversary Workshop, 27 - 29 Oct 2019, Antipolis, France.
Abstract
In GameStory: The 2019 Video Game Analytics Challenge, two main tasks are nominated to solve in the challenge, which are replay detection - multi-stream synchronization, and game story summarization. In this paper, we propose a data-driven based approach to solve the first task: replay detection - multi-stream synchronization. Our solution aims to determine the replays which lie between two logo-transitional endpoints and synchronize them with their sources by extracting frames from videos, then applying image processing and retrieval remedies. In detail, we use the Bag of Visual Words approach to detect the logo-transitional endpoints, which contains multiple replays in between, then employ an Image Signature Matching algorithm for multi-stream synchronization and replay boundaries refinement. The best configuration of our proposed solution manages to achieve the second-highest scores in all evaluation metrics of the challenge.
Metadata
| Item Type: | Conference or Workshop Item (Speech) |
|---|---|
| Event Type: | Workshop |
| Refereed: | Yes |
| Subjects: | Computer Science > Algorithms Computer Science > Information retrieval |
| DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing Research Institutes and Centres > INSIGHT Centre for Data Analytics Research Institutes and Centres > ADAPT |
| Published in: | MediaEval 2019 Multimedia Benchmark Workshop. CEUR Workshop Proceedings 1(2670). CEUR-WS. |
| Publisher: | CEUR-WS |
| Official URL: | http://ceur-ws.org/Vol-2670/MediaEval_19_paper_12.... |
| Copyright Information: | © 2019 The Authors. Creative Commons License Attribution 4.0 International (CC BY 4.0) |
| Funders: | Science Foundation Ireland under grant numbers SFI/12/RC2289 and 13/RC/2106., ADAPT Centre, DCU, INSIGHT Centre for Data Analytics |
| ID Code: | 23953 |
| Deposited On: | 26 Nov 2019 11:00 by Van-Tu Ninh . Last Modified 15 Dec 2021 15:48 |
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