We present a new hierarchical event detection approach for highly complex scenarios in pedestrian groups on the basis of airborne image sequences from UAVs. Related work on event detection for pedestrians is capable of learning and analyzing recurring motion paths to detect abnormal paths and of analyzing the type of motion interaction between pairs of pedestrians. However, these approaches can only describe basic motion and fail at the analysis of pedestrian groups with complex behavior. We overcome the limitations of the related work by using a dynamic pedestrian graph of a scene which contains basic pairwise pedestrian motion interaction labels in the first layer. In the second layer, pedestrian groups are analyzed based on the dynamic pedestrian graph in order to get higher-level information about group behavior. This is done by a heuristic assignment of predefined scenarios out of a model library to the data. The assignment is based on the motion interaction labels, on dynamic group motion parameters and on a set of subgraph features. Experimental results are shown based on a new UAV dataset which contains group motion of different complexity levels.