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Publication Detail
Deeplogger: Extracting user input logs from 2D gameplay videos
  • Publication Type:
    Conference
  • Authors:
    Intharah T, Brostow GJ
  • Publisher:
    ACM
  • Publication date:
    23/10/2018
  • Pagination:
    221, 230
  • Published proceedings:
    CHI PLAY 2018 - Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play
  • ISBN-13:
    9781450356244
  • Status:
    Published
  • Name of conference:
    CHI PLAY 2018, 2018 Annual Symposium on Computer-Human Interaction in Play
  • Conference place:
    Melbourne, VIC, Australia
  • Conference start date:
    28/10/2018
  • Conference finish date:
    31/10/2018
Abstract
©2018 Copyright is held by the owner/author(s). Publication rights licensed to ACM. 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.
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