  Landmark-based human action recognition in videos is a challenging task in
computer vision. One key step is to design a generic approach that generates
discriminative features for the spatial structure and temporal dynamics. To
this end, we regard the evolving landmark data as a high-dimensional path and
apply non-linear path signature techniques to provide an expressive, robust,
non-linear, and interpretable representation for the sequential events. We do
not extract signature features from the raw path, rather we propose path
disintegrations and path transformations as preprocessing steps. Path
disintegrations turn a high-dimensional path linearly into a collection of
lower-dimensional paths; some of these paths are in pose space while others are
defined over a multiscale collection of temporal intervals. Path
transformations decorate the paths with additional coordinates in standard ways
to allow the truncated signatures of transformed paths to expose additional
features. For spatial representation, we apply the signature transform to
vectorize the paths that arise out of pose disintegration, and for temporal
representation, we apply it again to describe this evolving vectorization.
Finally, all the features are collected together to constitute the input vector
of a linear single-hidden-layer fully-connected network for classification.
Experimental results on four datasets demonstrated that the proposed feature
set with only a linear shallow network and Dropconnect is effective and
achieves comparable state-of-the-art results to the advanced deep networks, and
meanwhile, is capable of interpretation.
