论文标题
drawmon：用于检测并发图像游戏中非典型草图内容的分布式系统
DrawMon: A Distributed System for Detection of Atypical Sketch Content in Concurrent Pictionary Games
论文作者
论文摘要
流行的基于素描的猜测游戏Pictionary为分析有限的沟通设置中的共享目标合作游戏提供了一个机会。但是，有些玩家偶尔会绘制非典型的草图内容。尽管这种内容偶尔在游戏环境中是相关的，但有时代表违反规则并损害游戏体验。为了及时且可扩展的方式解决此类情况，我们介绍了Drawmon，这是一种新颖的分布式框架，用于自动检测同时发生的图像游戏会话中的非典型草图内容。我们构建了专门的在线界面，以收集游戏会话数据并注释非典型的草图内容，从而导致了Indypict，这是第一个非典型的非典型草图内容数据集。我们使用Inypict训练Canvasnet，这是一个深度神经非典型内容检测网络。我们利用canvasnet作为抽纸的核心组成部分。我们对部署后游戏会话数据的分析表明DrawMon在可扩展监视和非典型草图内容检测中的有效性。除了Iptionary之外，我们的贡献还可以作为涉及共享和交互式白板的定制非典型内容响应系统的设计指南。代码和数据集可从https://drawm0n.github.io获得。
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.
