Title: Leveraging the Path Signature for Skeleton-based Human Action Recognition,
Abstract: Human action recognition in videos is one of the most challenging tasks in
computer vision. One important issue is how to design discriminative features
for representing spatial context and temporal dynamics. Here, we introduce a
path signature feature to encode information from intra-frame and inter-frame
contexts. A key step towards leveraging this feature is to construct the proper
trajectories (paths) for the data steam. In each frame, the correlated
constraints of human joints are treated as small paths, then the spatial path
signature features are extracted from them. In video data, the evolution of
these spatial features over time can also be regarded as paths from which the
temporal path signature features are extracted. Eventually, all these features
are concatenated to constitute the input vector of a fully connected neural
network for action classification. Experimental results on four standard
benchmark action datasets, J-HMDB, SBU Dataset, Berkeley MHAD, and NTURGB+D
demonstrate that the proposed approach achieves state-of-the-art accuracy even
in comparison with recent deep learning based models.