NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Institute for Infocomm Research · Nanyang Technological University
Abstract
Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes. Currently available depth-based and RGB+Dbased action recognition benchmarks have a number of limitations, including the lack of training samples, distinct class labels, camera views and variety of subjects. In this paper we introduce a large-scale dataset for RGB+D human action recognition with more than 56 thousand video samples and 4 million frames, collected from 40 distinct subjects. Our dataset contains 60 different action classes including daily, mutual, and health-related actions. In addition, we propose a new recurrent neural…
Citation impact
- FWCI
- 83.46
- Percentile
- 100%
- References
- 68
Authors
4Topics & keywords
- Computer science
- Artificial intelligence
- RGB color model
- Task (project management)
- Machine learning
- Scale (ratio)
- Deep learning
- Visualization