DrawMon: A Distributed System for Detection of Atypical Sketch Content in Concurrent Pictionary Games
Nikhil Bansal, Kartik Gupta, Kiruthika Kannan, Sivani Pentapati, Ravi, Kiran Sarvadevabhatla

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…
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