DrawMon: A Distributed System for Detection of Atypical Sketch Content in Concurrent Pictionary Games
This work addresses rule violations and experience issues in Pictionary games, with potential applications in interactive whiteboard systems, but it is incremental as it applies existing detection methods to a new domain-specific dataset.
The paper tackled the problem of detecting atypical sketch content in concurrent Pictionary games, which can impair gameplay, by introducing DrawMon, a distributed system that uses a deep neural network trained on a new dataset, and it demonstrated effectiveness in scalable monitoring post-deployment.
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.