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DrawMon: A Distributed System for Detection of Atypical Sketch Content in Concurrent Pictionary Games

arXiv 2211.05429 ↗First posted 10 Nov 2022cs.CV cs.GR cs.MMcited by 0

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Abstract (arXiv)

Pictionary, the popular sketch-based guessing game, provides an opportunity to analyze shared goal cooperative game play in restricted communication settings. However, some players occasionally draw atypical sketch content. While such content is occasionally relevant in the game context, it sometimes represents a rule violation and impairs the game experience. To address such situations in a timely and scalable manner, we introduce DrawMon, a novel distributed framework for automatic detection of atypical sketch content in concurrently occurring Pictionary game sessions. We build specialized online interfaces to collect game session data and annotate atypical sketch content, resulting in AtyPict, the first ever atypical sketch content dataset. We use AtyPict to train CanvasNet, a deep neural atypical content detection network. We utilize CanvasNet as a core component of DrawMon. Our analysis of post deployment game session data indicates DrawMon's effectiveness for scalable monitoring and atypical sketch content detection. Beyond Pictionary, our contributions also serve as a design guide for customized atypical content response systems involving shared and interactive whiteboards. Code and datasets are available at https://drawm0n.github.io.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 58 references that are in the map. First posted on arXiv 10 Nov 2022.

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23 of 58 references.

References · 23

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11 more
  1. 2018Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instructioncited by 61
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