  This paper presents a novel hierarchical approach for collective behavior
recognition based solely on ground-plane trajectories. In the first layer of
our classifier, we introduce a novel feature called Personal Interaction
Descriptor (PID), which combines the spatial distribution of a pair of
pedestrians within a temporal window with a pyramidal representation of the
relative speed to detect pairwise interactions. These interactions are then
combined with higher level features related to the mean speed and shape formed
by the pedestrians in the scene, generating a Collective Behavior Descriptor
(CBD) that is used to identify collective behaviors in a second stage. In both
layers, Random Forests were used as classifiers, since they allow features of
different natures to be combined seamlessly. Our experimental results indicate
that the proposed method achieves results on par with state of the art
techniques with a better balance of class errors. Moreover, we show that our
method can generalize well across different camera setups through cross-dataset
experiments.
