Skeleton-Based Action Recognition Based on Distance Vector and Multihigh View Adaptive Networks

Skeleton-based human action recognition has attracted much attention in the field of computer vision. Most of the previous studies are based on fixed skeleton graphs so that only the local physical dependencies among joints can be captured, resulting in the omission of implicit joint correlations. In addition, under different views, the content of the same action is very different. In some views, keypoints will be blocked, which will cause recognition errors. In this paper, an action recognition method based on distance vector and multihigh view adaptive network (DV-MHNet) is proposed to address this challenging task. Among the mentioned techniques, the multihigh (MH) view adaptive networks are constructed to automatically determine the best observation view at different heights, obtain complete keypoints information of the current frame image, and enhance the robustness and generalization of the model to recognize actions at different heights. Then, the distance vector (DV) mechanism is introduced on this basis to establish the relative distance and relative orientation between different keypoints in the same frame and the same keypoints in different frame to obtain the global potential relationship of each keypoint, and finally by constructing the spatial temporal graph convolutional network to take into account the information in space and time, the characteristics of the action are learned. This paper has done the ablation study with traditional spatial temporal graph convolutional networks and with or without multihigh view adaptive networks, which reasonably proves the effectiveness of the model. The model is evaluated on two widely used action recognition benchmarks (NTU-RGB + D and PKU-MMD). Our method achieves better performance on both datasets.