Memory Group Sampling Based Online Action Recognition Using Kinetic Skeleton Features

TL;DR

This paper introduces a novel online action recognition method that combines multi-scale skeleton features with a memory group sampling technique, leveraging an improved 1D CNN to efficiently capture both spatial and temporal information for human actions.

Contribution

It proposes a new memory group sampling approach and combines multi-scale skeleton features with an enhanced 1D CNN for improved online action recognition.

Findings

The method achieves competitive accuracy on public datasets.

It is faster and more efficient than existing approaches.

The approach effectively captures long-term contextual information.

Abstract

Online action recognition is an important task for human centered intelligent services, which is still difficult to achieve due to the varieties and uncertainties of spatial and temporal scales of human actions. In this paper, we propose two core ideas to handle the online action recognition problem. First, we combine the spatial and temporal skeleton features to depict the actions, which include not only the geometrical features, but also multi-scale motion features, such that both the spatial and temporal information of the action are covered. Second, we propose a memory group sampling method to combine the previous action frames and current action frames, which is based on the truth that the neighbouring frames are largely redundant, and the sampling mechanism ensures that the long-term contextual information is also considered. Finally, an improved 1D CNN network is employed for training and testing using the features from sampled frames. The comparison results to the state of the art methods using the public datasets show that the proposed method is fast and efficient, and has competitive performance