  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.
