One-shot action recognition aims to recognize new action categories from a single reference example, typically referred to as the anchor example. This work presents a novel approach for one-shot action recognition in the wild that computes motion representations robust to variable kinematic conditions. One-shot action recognition is then performed by evaluating anchor and target motion representations. We also develop a set of complementary steps that boost the action recognition performance in the most challenging scenarios. Our approach is evaluated on the public NTU-120 one-shot action recognition benchmark, outperforming previous action recognition models. Besides, we evaluate our framework on a real use-case of therapy with autistic people. These recordings are particularly challenging due to high-level artifacts from the patient motion. Our results provide not only quantitative but also online qualitative measures, essential for the patient evaluation and monitoring during the actual therapy.
@article{arxiv.2102.08997,
title = {One-shot action recognition in challenging therapy scenarios},
author = {Alberto Sabater and Laura Santos and Jose Santos-Victor and Alexandre Bernardino and Luis Montesano and Ana C. Murillo},
journal= {arXiv preprint arXiv:2102.08997},
year = {2021}
}