Visualizing Competitive Behaviors in Multi-User Virtual Environments
Nathan Hoobler, Greg Humphreys, Maneesh Agrawala
Abstract
We present a system for enhancing observation of user interactions in virtual environments. In particular, we focus on analyzing behavior patterns in the popular team-based first-person perspective game Return to Castle Wolfenstein: Enemy Territory. This game belongs to a genre characterized by two moderate-sized teams (usually 6 to 12 players each) competing over a set of objectives. Our system allows spectators to visualize global features, as opposed to the limited local view that traditional spectating modes provide. We also add overlay visualizations of semantic information related to the action that might be important to a spectator in order to reduce the information overload that plagues traditional overview visualizations. These overlays can visualize non-obvious information such as player distribution over time and areas of intense combat activity, and also highlight important features like player paths, fire coverage, etc. This added information allows spectators to identify important game events more easily and reveals large-scale player behaviors that might otherwise be overlooked.
In first-person games, observation modes are typically restricted to an over-the-shoulder chase camera (left) or a floatingplayer view (center). Both views make it very difficult to understand complex team-oriented actions that have an inherent global nature. We present a novel game observation system (right) that extracts high-level semantic information about the action taking place in a game and displays it visually. By emphasizing important low-level details and overlaying them with high level action summaries, we provide a unique and insightful new view of the environment and behaviors therein. Using our system, it can now be seen that the red team is holding the bridge at the center of the map against a frontal assault by blue, but is also being flanked from the North by a lone blue player.