  By thoroughly revisiting the classic human action recognition paradigm, this
paper aims at proposing a new approach for the design of effective action
classification systems. Taking as testbed publicly available three-dimensional
(MoCap) action/activity datasets, we analyzed and validated different
training/testing strategies. In particular, considering that each human action
in the datasets is performed several times by different subjects, we were able
to precisely quantify the effect of inter- and intra-subject variability, so as
to figure out the impact of several learning approaches in terms of
classification performance. The net result is that standard testing strategies
consisting in cross-validating the algorithm using typical splits of the data
(holdout, k-fold, or one-subject-out) is always outperformed by a
"personalization" strategy which learns how a subject is performing an action.
In other words, it is advantageous to customize (i.e., personalize) the method
to learn the actions carried out by each subject, rather than trying to
generalize the actions executions across subjects. Consequently, we finally
propose an action recognition framework consisting of a two-stage
classification approach where, given a test action, the subject is first
identified before the actual recognition of the action takes place. Despite the
basic, off-the-shelf descriptors and standard classifiers adopted, we noted a
relevant increase in performance with respect to standard state-of-the-art
algorithms, so motivating the usage of personalized approaches for designing
effective action recognition systems.
