NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis

NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Title:
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Other Titles:
DOI:
Publication Date:
27 June 2016
Citation:
arXiv:1604.02808
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+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 handcrafted features on the suggested cross-subject and crossview 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.
License type:
PublisherCopyrights
Funding Info:
Description:
ISBN:

Files uploaded:

File Size Format Action
amir-cvpr16.pdf 2.83 MB PDF Open