Logo
Logo

Goldsmiths - University of London

Deep Unsupervised Multi-View Detection of Video Game Stream Highlights

Ringer, Charles and Nicolaou, Mihalis. 2018. 'Deep Unsupervised Multi-View Detection of Video Game Stream Highlights'. In: Foundations on Digital Games 2018. Malmo, Sweden. [Conference or Workshop Item] (In Press)

[img]
Preview
Text
deep-unsupervised-multiGRO.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.

Download (6MB) | Preview

Abstract or Description

We consider the problem of automatic highlight-detection in video game streams. Currently, the vast majority of highlight-detection systems for games are triggered by the occurrence of hard-coded game events (e.g., score change, end-game), while most advanced tools and techniques are based on detection of highlights via visual analysis of game footage. We argue that in the context of game streaming, events that may constitute highlights are not only dependent on game footage, but also on social signals that are conveyed by the streamer during the play session (e.g., when interacting with viewers, or when commenting and reacting to the game). In this light, we present a multi-view unsupervised deep learning methodology for novelty-based highlight detection. The method jointly analyses both game footage and social signals such as the players facial expressions and speech, and shows promising results for generating highlights on streams of popular games such as Player Unknown's Battlegrounds.

Item Type: Conference or Workshop Item (Paper)

Identification Number (DOI):

https://doi.org/10.1145/3235765.3235781

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
1 May 2018Accepted
7 August 2018Published

Event Location:

Malmo, Sweden

Item ID:

24109

Date Deposited:

19 Sep 2018 08:58

Last Modified:

19 Sep 2018 09:01

URI: http://research.gold.ac.uk/id/eprint/24109

View statistics for this item...

Edit Record Edit Record (login required)