Action recognition in freestyle wrestling using silhouette-skeleton features

Abstract

Despite many advances made in Human Action Recognition (HAR), there are still challenges encouraging researchers to explore new methods. In this study, a new feature descriptor based on the silhouette skeleton called Histogram of Graph Nodes (HGN) is proposed. Unlike similar methods, which are strictly based on the articulated human body model, we extracted discriminative features solely using the foreground silhouettes. To this purpose, first, the skeletons of the silhouettes are converted into a graph, representing approximately articulated human body skeleton. By partitioning the region of the graph, the HGN is calculated in each frame. After that, we obtain the final feature vector by combining the HGNs in time. On the other hand, the recognition of two-person sports techniques is one of the areas that has not received adequate attention. To this end, we investigate the recognition of techniques in wrestling as a new computer vision application. In this regard, a dataset of the Freestyle Wrestling techniques (FSW) is introduced. We conducted extensive experiments using the proposed method on the provided dataset. In addition, we examined the proposed feature descriptor on the SBU and THETIS datasets, and the MHI-based features on the FSW dataset. We achieved 84.9% accuracy on FSW dataset while the results are 90.8% for SBU and 44% for THETIS datasets. The fact that experimental results are superior or comparable to other similar methods indicates the effectiveness of the proposed approach.