Revisiting human action recognition: Personalization VS. Generalization

Abstract

By thoroughly revisiting the classic human action recognition paradigm, we analyzed different training/testing strategies, discovering that standard (cross-validating) testing strategies are not always the suitable validation procedures to assess an algorithm’s performance. As a consequence, we design a novel action recognition architecture, applying a “personalized” strategy to learn how any subject performs any action. We discover that 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. Leveraging on that, we 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 score a favorable performance with respect to the state-of-the-art as to certify the soundness of our approach.