Release Agreement for "NTU RGB+D" and "NTU RGB+D 120" Action Recognition Datasets
"NTU RGB+D" and "NTU RGB+D 120" action recognition datasets consist of RGB videos, depth map sequences, 3D skeletal data, and infrared videos. These two datasets are 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.
Both "NTU RGB+D" and "NTU RGB+D 120" action recognition datasets are the property of the Rapid-Rich Object Search (ROSE) Lab at the Nanyang Technological University, Singapore.
Usage for Academic Reseach
Both "NTU RGB+D" and "NTU RGB+D 120" are released for academic research only, and are free to researchers from educational or research institutes for non-commercial purposes.
Terms and Conditions:
- Without the expressed permission of the ROSE Lab, any of the following will be considered illegal: redistribution, derivation or generation of a new dataset from this dataset, and commercial usage of any of these datasets in any way or form, either partially or in its entirety.
- For the sake of privacy, images of all subjects in any of these datasets are only allowed for the demonstration in academic publications and presentations.
- All users of "NTU RGB+D" and "NTU RGB+D 120" action recognition datasets agree to indemnify, defend and hold harmless, the ROSE Lab and its officers, employees, and agents, individually and collectively, from any and all losses, expenses, and damages.
All publications using "NTU RGB+D" or "NTU RGB+D 120" Action Recognition Database should include the following acknowledgement: “(Portions of) the research in this paper used the NTU RGB+D (or NTU RGB+D 120) Action Recognition Dataset made available by the ROSE Lab at the Nanyang Technological University, Singapore.”
Furthermore, these publications should cite the following references:
Amir Shahroudy, Jun Liu, Tian-Tsong Ng, Gang Wang, "NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis", IEEE Conference on Computer Vision and Pattern Recognition, 2016 [PDF] [bibtex].
Jun Liu, Amir Shahroudy, Mauricio Perez, Gang Wang, Ling-Yu Duan, Alex C. Kot, "NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. [PDF] [bibtex].
Requestor may also wish to cite the following related work:
- Amir Shahroudy, Tian-Tsong Ng, Qingxiong Yang, Gang Wang, “Multimodal Multipart Learning for Action Recognition in Depth Videos”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016
- Amir Shahroudy, Tian-Tsong Ng, Yihong Gong, Gang Wang, “Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018
- Jun Liu, Gang Wang, Ping Hu, Ling-Yu Duan, Alex C. Kot, “Global Context-Aware Attention LSTM Networks For 3D Action Recognition”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
- Jun Liu, Amir Shahroudy, Dong Xu, Gang Wang, “Spatio-Temporal LSTM With Trust Gates For 3D Human Action Recognition”, in European Conference on Computer Vision (ECCV), 2016
- Jun Liu, Amir Shahroudy, Dong Xu, Alex C. Kot, Gang Wang, “Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018
- Jun Liu, Gang Wang, Ling-Yu Duan, Kamila Abdiyeva, Alex C. Kot, “Skeleton-Based Human Action Recognition with Global Context-aware Attention LSTM Networks, IEEE Transactions on Image Processing (TIP), 2018
By completing this online form, you have accepted the above terms & conditions and agree to include the above acknowledgements in your publications.