A Sliding Window Based Approach With Majority Voting for Online Human Action Recognition using Spatial Temporal Graph Convolutional Neural Networks

Nowadays, Human Action Recognition (HAR) has become an important issue since it is widely used in video surveillance, humanrobot collaboration in industry, etc. Developing such accurate and efficient algorithms remains a difficult task because of the high variability of the human shapes, postures, as well as the complexity of their movements but more importantly when using continuous/untrimmed data streams. Since HAR from Segmented/trimmed sequences has been intensively studied and developed in the recent years, Online HAR in the other hand remains a challenging task and is less developed. In this paper, we propose a Sliding Window and Majority Voting skeleton-based approach for Online HAR using Spatial Temporal Graph Convolutional Neural Networks (STGCNSWMV). Our method is evaluated on two Online skeleton-based datasets named OAD and UOW. In comparison with existing methods, the obtained results exceed state-of-the-art algorithms and show the efficiency of the proposed approach.