Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN). In this paper, we propose a novel convolutional neural networks (CNN) based framework for both action classifica tion and detection. Raw skeleton coordinates as well as skele ton motion are fed directly into CNN for label prediction. A novel skeleton transformer module is designed to rearrange and select important skeleton joints automatically. With a simple 7-layer network, we obtain 89.3% accuracy on valida tion set of the NTU RGB+D dataset. For action detection in untrimmed videos, we develop a window proposal network to extract temporal segment proposals, which are further clas sified within the same network. On the recent PKU-MMD dataset, we achieve 93.7% mAP, surpassing the baseline by a large margin.
Chao et al. REF propose a novel convolutional neural networks (CNN) based framework for both action classification and detection.
Skeleton-based action recognition with convolutional neural networks
2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
Computer Science