Phase Randomization: A data augmentation for domain adaptation in human action recognition

Human action recognition models often suffer from achieving both accurate recognition and subject independence when the amount of training data is limited. In this paper, we propose a data-efficient domain adaptation approach to learning a subject-agnostic action recognition classifier. The core component of our approach is a novel data augmentation called Phase Randomization. On the basis of the observation that individual body size is highly correlated with the amplitude component of the motion sequence, we disentangle the individuality and action features by using contrastive self-supervised learning with data augmentation that randomizes only the phase component of the motion sequence. This enables us to estimate the subject label of each motion sequence and to train a subject-agnostic action recognition classifier by performing adversarial learning with the estimated subject labels. We empirically demonstrate the superiority of our method on two different action recognition tasks (skeleton-based action recognition and sensor-based activity recognition).