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

TL;DR

DrawMon is a distributed system that automatically detects atypical sketch content in concurrent Pictionary games, improving game experience and providing a scalable monitoring solution.

Contribution

We introduce DrawMon, a novel distributed framework with specialized interfaces, a new dataset AtyPict, and a deep neural network CanvasNet for detecting atypical sketch content.

Findings

Effective detection of atypical sketches in real-time

Scalable monitoring in concurrent game sessions

Generalizable design for shared interactive whiteboards

Abstract

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.