Pairwise features for human action recognition
Résumé
Existing action recognition approaches mainly rely on the
discriminative power of individual local descriptors
extracted from spatio-temporal interest points (STIP),
while the geometric relationships among the local features
are ignored. This paper presents new features, called
pairwise features (PWF), which encode both the appearance
and the spatio-temporal relations of the local features for
action recognition. First STIPs are extracted, then PWFs
are constructed by grouping pairs of STIPs which are both
close in space and close in time. We propose a combination
of two codebooks for video representation. Experiments on
two standard human action datasets: the KTH dataset and the
Weizmann dataset show that the proposed approach
outperforms most existing methods.