Release Agreement for NTU RGB+D Action Recognition Dataset

NTU RGB+D action recognition dataset consists of 56,880 action samples containing RGB videos, depth map sequences, 3D skeletal data, and infrared videos for each sample. This dataset is captured by 3 Microsoft Kinect v.2 cameras concurrently. The resolution of RGB videos are 1920×1080, depth maps and IR videos are all in 512×424, and 3D skeletal data contains the three dimensional locations of 25 major body joints, at each frame.

The NTU RGB+D action recognition dataset is the property of the Rapid-Rich Object Search (ROSE) Lab at the Nanyang Technological University, Singapore.

Usage for Academic Reseach

The image database is released for academic research only, and is free to researchers from educational or research institutes for non-commercial purposes.

Terms and Conditions:

The use of this dataset is governed by the following terms and conditions:

Related Publications

All publications using the NTU RGB+D Action Recognition Database should include the following acknowledgement: “(Portions of) the research in this paper used the NTU RGB+D Action Recognition Dataset made available by the ROSE Lab at the Nanyang Technological University, Singapore.”

Furthermore, these publications should cite the following reference:
Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang, "NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis", in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 [PDF] [bibtex].

Requestor may also wish to cite the following related work:


By completing this online form, you have accepted the above terms & conditions and agree to include the above acknowledgements in your publications.

We need to validate all requests. Therefore we require you to provide full name of organisation and affiliated email address.

Project Title
Requester's Details
Title
First Name Last Name
Designation example: post-graduate student, research associate, research fellow, faculty, system analyst, data scientist
Email Address
Organisation
Country
Supervisor's Details (Leave all fields below blank if Supervisor is same as Requester)
Title
First Name Last Name
Email Address
Designation example: research fellow, faculty, lead data scientist, chief data officer