Activity Identification and Visualization

Understanding activity from observing the motion of agents is simple for people to do, yet the procedure is difficult to codify. It is impossible to enumerate all possible motion patterns which could occur, or to dictate the ex- plicit behavioural meaning of each motion. We develop visualization tools to assist a human user in labelling detected behaviours and identifying useful at- tributes. We also apply machine learning to the classification of motion into motion and behavioural labels. Issues include feature selection and classifier performance.