In this work we consider a machine learning setting where data are represented as graphs. First, we derive a kernel function
which evaluates the similarity between graphs, while capturing pair-wise constraints between graph nodes. Second, we apply it to the problem of classifying collective activities: on this respect we first represent groups of people located in a spatial neighborhood as graphs, and then train a multi-class classifier able to capture the behavior of the groups.
We evaluate our approach on a benchmark dataset and report a comparative analysis with other state-of-art methods which highlights the benefits of our approach.