  This paper proposes combining spatio-temporal appearance (STA) descriptors
with optical flow for human action recognition. The STA descriptors are local
histogram-based descriptors of space-time, suitable for building a partial
representation of arbitrary spatio-temporal phenomena. Because of the
possibility of iterative refinement, they are interesting in the context of
online human action recognition. We investigate the use of dense optical flow
as the image function of the STA descriptor for human action recognition, using
two different algorithms for computing the flow: the Farneb\"ack algorithm and
the TVL1 algorithm. We provide a detailed analysis of the influencing optical
flow algorithm parameters on the produced optical flow fields. An extensive
experimental validation of optical flow-based STA descriptors in human action
recognition is performed on the KTH human action dataset. The encouraging
experimental results suggest the potential of our approach in online human
action recognition.
