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Deeplogger: Extracting user input logs from 2D gameplay videos

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. Green open access

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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.ucl.ac.uk/id/eprint/10088944
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