Detecting team behavior using focus of attention

An autonomous mobile robot, working with human teammates, should be equipped to intelligently react to changes in team behavior without relying on directives from human team members. To respond appropriately to changes in team behavior, the robot should detect when these situations occur, and correctly classify the new team behavior. We demonstrate a method for detecting and classifying behavior changes in a simulated team, using the team´s focus of attention. The method draws from Kim et al. (2010), who developed an algorithm for propagating the motion of soccer players through a vector field in order to predict locations of future action in a soccer game. Using this propagation method, our implementation extends this work by extracting statistical features from the motion information, and, looking back over a window of prior feature values, detects changes in the team behavior and classifies group activity according to a set of possible behaviors.