In this paper we address the problem in motion recognition using event-based local motion representations. We assume that similar patterns of motion contain similar events with consistent motion across image sequences. Using this assumption, we formulate the problem of motion recognition as a matching of corresponding events in image sequences. To enable the matching, we present and evaluate a set of motion descriptors exploiting the spatial and the temporal coherence of motion measurements between corresponding events in image sequences. As motion measurements may depend on the relative motion of the camera, we also present a mechanism for local velocity adaptation of events and evaluate its influence when recognizing image sequences subjected to different camera motions. When recognizing motion, we compare the performance of nearest neighbor (NN) classifier with the performance of support vector machine (SVM).We also compare event-based motion representations to motion representations by global histograms. An experimental evaluation on a large video database with human actions demonstrates the advantage of the proposed scheme for event-based motion representation in combination with SVM classification. The particular advantage of event-based representations and velocity adaptation is further emphasized when recognizing human actions in unconstrained scenes with complex and non-stationary backgrounds.