Abstract. We present a novel action representation based on encoding the global
temporal movement of an action. We represent an action as a set of movement
pattern histograms that encode the global temporal dynamics of an action. Our
key observation is that temporal dynamics of an action are robust to variations
in appearance and viewpoint changes, making it useful for action recognition
and retrieval. We pose the problem of computing similarity between action representations
as a maximum matching problem in a bipartite graph. We demonstrate
the effectiveness of our method for cross-view action recognition on the
IXMAS dataset.We also show how our representation complements existing bagof-
features representations on the UCF50 dataset. Finally we show the power
of our representation for action retrieval on a new real-world dataset containing
repetitive motor movements emitted by children with autism in an unconstrained
classroom setting.