AN ONLINE SKELETON-BASED ACTION RECOGNITION ALGORITHM WITH MULTI-FEATURE EARLY FUSION
Existing skeleton-based action recognition methods require large computation,which makes them unsuitable for online applications.Aiming at this problem,this paper proposes an online skeleton-based action recognition method with multi-feature early fusion.The algorithm integrated different types of input feature through the early embedding layer and combined the max pooling and hierarchical pooling to extract multi-semantic spatial information.The selection strategy of skeleton sequences was designed based on the characteristics of daily actions.A new 3D skeleton dataset,NTU-GAST Skeleton,was made for online action recognition.Experiments on NTU60 and 120 RGB+D dataset indicate that the proposed method achieves higher recognition accuracy with less computational complexity.
国家科技期刊平台
NSTL
万方数据
维普
