Deep-Temporal LSTM for Daily Living Action Recognition
arXiv:1802.00421
Abstract
In this paper, we propose to improve the traditional use of RNNs by employing a many to many model for video classification. We analyze the importance of modeling spatial layout and temporal encoding for daily living action recognition. Many RGB methods focus only on short term temporal information obtained from optical flow. Skeleton based methods on the other hand show that modeling long term skeleton evolution improves action recognition accuracy. In this work, we propose a deep-temporal LSTM architecture which extends standard LSTM and allows better encoding of temporal information. In addition, we propose to fuse 3D skeleton geometry with deep static appearance. We validate our approach on public available CAD60, MSRDailyActivity3D and NTU-RGB+D, achieving competitive performance as compared to the state-of-the art.
arXiv commentSubmitted in conference
References in corpus (7)
- Adam: A Method for Stochastic Optimization
- Sequence to Sequence Learning with Neural Networks
- Two-Stream Convolutional Networks for Action Recognition in Videos
- UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
- Unstructured Human Activity Detection from RGBD Images
- NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
- Convolutional Two-Stream Network Fusion for Video Action Recognition