(YIP-09) Improving Synthesis and Recognition of Crowded Scenes using Statistical Models of Group Behavior
نویسنده
چکیده
Virtual training environments are an important tool for training military personnel in a costeffective way, and realistic crowd modeling and population simulation are crucial components for developing training scenarios set in urban environments. The key objectives of this project were to: 1. create computational models of human group behavior; 2. analyze group behavior in simulation environments; 3. synthetically generate realistic group behavior in software agents; 4. simulate populations in large urban settings using agent-based models. In the future, these models will enable software developers to significantly improve the realism of agents in military simulations and the accuracy of behavior recognition in surveillance applications by better characterizing how social context affects action selection for humans in group settings.
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