Group activity recognition on outdoor scenes

Kyle Stephens, Adrian G. Bors

Abstract: In this research study, we propose an automatic group activity recognition approach by modelling the interdependencies of group activity features over time. Unlike in simple human activity recognition approaches, the distinguishing characteristics of group activities are often determined by how the movement of people are influenced by one another. We propose to model the group interdependences in both motion and location spaces. These spaces are extended to time-space and time-movement spaces and modelled using Kernel Density Estimation (KDE). Such representations are then fed into a machine learning classifier which identifies the group activity. Unlike other approaches to group activity recognition, we do not rely on the manual annotation of pedestrian tracks from the video sequence.