Hard Sample Mining and Learning for Skeleton-Based Human Action Recognition and Identification

Abstract

The analysis of human actions, such as recognition and person identification, is important but challenging. Previous studies usually separate person identification from action recognition. In this paper, we propose a skeleton-based end-to-end model that can simultaneously implement both person identification and action recognition and strengthen the learning of hard samples. Inspired by the recent application of recurrent neural networks and convolution neural networks to action analysis, the models of the temporal and spatial domains are created by modeling the global, local, and detailed feature-related information of the human skeleton. Moreover, during training, we add a mining and learning mechanism for hard samples that mines and focuses on them to avoid overfitting. This mining and learning mechanism enables the model to avoid a bottleneck and improve performance. The proposed model is experimentally verified on three datasets, where the results show that it can yield the state-of-the-art performance.