Intharah, T;
Brostow, GJ;
(2018)
Deeplogger: Extracting user input logs from 2D gameplay videos.
In:
Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play.
(pp. pp. 221-230).
ACM: New York, NY, USA.
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Abstract
Game and player analysis would be much easier if user interactions were electronically logged and shared with game researchers. Understandably, sniffing software is perceived as invasive and a risk to privacy. To collect player analytics from large populations, we look to the millions of users who already publicly share video of their game playing. Though labor-intensive, we found that someone with experience of playing a specific game can watch a screen-cast of someone else playing, and can then infer approximately what buttons and controls the player pressed, and when. We seek to automatically convert video into such game-play transcripts, or logs. We approach the task of inferring user interaction logs from video as a machine learning challenge. Specifically, we propose a supervised learning framework to first train a neural network on videos, where real sniffer/instrumented software was collecting ground truth logs. Then, once our DeepLogger network is trained, it should ideally infer log-activities for each new input video, which features gameplay of that game. These user-interaction logs can serve as sensor data for gaming analytics, or as supervision for training of game-playing AI’s. We evaluate the DeepLogger system for generating logs from two 2D games, Tetris [23] and Mega Man X [6], chosen to represent distinct game genres. Our system performs as well as human experts for the task of video-to-log transcription, and could allow game researchers to easily scale their data collection and analysis up to massive populations.
| Type: | Proceedings paper |
|---|---|
| Title: | Deeplogger: Extracting user input logs from 2D gameplay videos |
| Event: | CHI PLAY '18 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3242671.3242674 |
| Publisher version: | https://doi.org/10.1145/3242671.3242674 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10088944 |
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