NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
This provides a large-scale dataset to enable data-hungry learning techniques for depth-based and RGB+D human activity analysis, addressing limitations in existing benchmarks.
The authors tackled the lack of large-scale datasets for RGB+D human action recognition by introducing NTU RGB+D, containing over 56,000 video samples and 4 million frames from 40 subjects across 60 action classes, and showed that deep learning methods outperform hand-crafted features on cross-subject and cross-view evaluations.
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+D-based 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 network structure to model the long-term temporal correlation of the features for each body part, and utilize them for better action classification. Experimental results show the advantages of applying deep learning methods over state-of-the-art hand-crafted features on the suggested cross-subject and cross-view evaluation criteria for our dataset. The introduction of this large scale dataset will enable the community to apply, develop and adapt various data-hungry learning techniques for the task of depth-based and RGB+D-based human activity analysis.