NTU RGB+D: A large scale dataset for 3D human activity analysis

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 1010-1019

  Shahroudy, Amir ^a,b   Liu, Jun ^a   Ng, Tian Tsong ^b   Wang, Gang ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; GESTURE RECOGNITION; INFORMATION ANALYSIS; LEARNING SYSTEMS; MOTION ESTIMATION; RECURRENT NEURAL NETWORKS;

ACTION CLASSIFICATIONS; ACTION RECOGNITION; EVALUATION CRITERIA; HUMAN ACTIVITY ANALYSIS; HUMAN-ACTION RECOGNITION; LARGE-SCALE DATASET; LEARNING TECHNIQUES; TEMPORAL CORRELATIONS;

PATTERN RECOGNITION;

EID: 84986252370     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.115     Document Type: Conference Paper

References (49)

1. J. Aggarwal and L. Xia. Human activity recognition from 3d data: A review. PR Letters, 2014.
2. W. Byeon, T. M. Breuel, F. Raue, and M. Liwicki. Scene labeling with lstm recurrent neural networks. In CVPR, 2015.
3. Z. Cai, J. Han, L. Liu, and L. Shao. Rgb-d datasets using microsoft kinect or similar sensors: A survey. Multimedia Tools and Applications, 2016.
4. C. Chen, R. Jafari, and N. Kehtarnavaz. Utd-mhad: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In ICIP, Sept 2015.
5. L. Chen, H. Wei, and J. Ferryman. A survey of human motion analysis using depth imagery. PR Letters, 2013.
6. Z. Cheng, L. Qin, Y. Ye, Q. Huang, and Q. Tian. Human daily action analysis with multi-view and color-depth data. In ECCV Workshops. 2012.
7. R. Collobert, K. Kavukcuoglu, and C. Farabet. Torch7: A matlab-like environment for machine learning. In BigLearn, NIPS Workshop, 2011.
8. J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell. Long-term recurrent convolutional networks for visual recognition and description. In CVPR, 2015.
9. Y. Du, W. Wang, and L. Wang. Hierarchical recurrent neural network for skeleton based action recognition. In CVPR, 2015.
10. G. Evangelidis, G. Singh, and R. Horaud. Skeletal quads: Human action recognition using joint quadruples. In ICPR, 2014.
11. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. JMLR, 2008.
12. F. Han, B. Reily, W. Hoff, and H. Zhang. Space-Time Representation of People Based on 3D Skeletal Data: A Review. ArXiv, 2016.
13. J. Han, L. Shao, D. Xu, and J. Shotton. Enhanced computer vision with microsoft kinect sensor: A review. IEEE Transactions on Cybernetics, 2013.
14. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 1997.
15. J.-F. Hu, W.-S. Zheng, J. Lai, and J. Zhang. Jointly learning heterogeneous features for rgb-d activity recognition. In CVPR, 2015.
16. A. Karpathy, J. Johnson, and F. Li. Visualizing and understanding recurrent networks. ArXiv, 2015.
17. Y. Kong and Y. Fu. Bilinear heterogeneous information machine for rgb-d action recognition. In CVPR, 2015.
18. H. S. Koppula, R. Gupta, and A. Saxena. Learning human activities and object affordances from rgb-d videos. IJRR, 2013.
19. W. Li, Z. Zhang, and Z. Liu. Action recognition based on a bag of 3d points. In CVPR Workshops, 2010.
20. C. Lu, J. Jia, and C.-K. Tang. Range-sample depth feature for action recognition. In CVPR, 2014.
21. R. Lun and W. Zhao. A survey of applications and human motion recognition with microsoft kinect. IJPRAI, 2015.
22. J. Luo, W. Wang, and H. Qi. Group sparsity and geometry constrained dictionary learning for action recognition from depth maps. In ICCV, 2013.
23. B. Ni, G. Wang, and P. Moulin. Rgbd-hudaact: A color-depth video database for human daily activity recognition. In ICCV Workshops, 2011.
24. E. Ohn-Bar and M. Trivedi. Joint angles similarities and hog2 for action recognition. In CVPR Workshops, 2013.
25. O. Oreifej and Z. Liu. Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences. In CVPR, 2013.
26. H. Rahmani, A. Mahmood, D. Huynh, and A. Mian. Histogram of oriented principal components for cross-view action recognition. TPAMI, 2016.
27. H. Rahmani, A. Mahmood, D. Q. Huynh, and A. Mian. Real time action recognition using histograms of depth gradients and random decision forests. In WACV, 2014.
28. H. Rahmani, A. Mahmood, D. Q Huynh, and A. Mian. Hopc: Histogram of oriented principal components of 3d pointclouds for action recognition. In ECCV. 2014.
29. H. Rahmani and A. Mian. Learning a non-linear knowledge transfer model for cross-view action recognition. In CVPR, 2015.
30. H. Rahmani and A. Mian. 3d action recognition from novel viewpoints. In CVPR, June 2016.
31. A. Shahroudy, T.-T. Ng, Y. Gong, and G. Wang. Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos. ArXiv, 2016.
32. A. Shahroudy, T. T. Ng, Q. Yang, and G. Wang. Multimodal multipart learning for action recognition in depth videos. TPAMI, 2016.
33. A. Shahroudy, G. Wang, and T.-T. Ng. Multi-modal feature fusion for action recognition in rgb-d sequences. In ISCCSP, 2014.
34. J. Sung, C. Ponce, B. Selman, and A. Saxena. Human activity detection from rgbd images. In AAAI Workshops, 2011.
35. I. Sutskever, O. Vinyals, and Q. V. V. Le. Sequence to sequence learning with neural networks. In NIPS. 2014.
36. V. Veeriah, N. Zhuang, and G.-J. Qi. Differential recurrent neural networks for action recognition. In ICCV, 2015.
37. R. Vemulapalli, F. Arrate, and R. Chellappa. Human action recognition by representing 3d skeletons as points in a lie group. In CVPR, 2014.
38. J. Wang, Z. Liu, Y. Wu, and J. Yuan. Mining actionlet ensemble for action recognition with depth cameras. In CVPR, 2012.
39. J. Wang, Z. Liu, Y. Wu, and J. Yuan. Learning actionlet ensemble for 3d human action recognition. TPAMI, 2014.
40. J. Wang, X. Nie, Y. Xia, Y. Wu, and S.-C. Zhu. Cross-view action modeling, learning, and recognition. In CVPR, 2014.
41. K. Wang, X. Wang, L. Lin, M. Wang, andW. Zuo. 3d human activity recognition with reconfigurable convolutional neural networks. In ACM MM, 2014.
42. P. Wang, W. Li, Z. Gao, J. Zhang, C. Tang, and P. Ogunbona. Action recognition from depth maps using deep convolutional neural networks. In THMS, 2015.
43. P. Wei, Y. Zhao, N. Zheng, and S.-C. Zhu. Modeling 4d human-object interactions for event and object recognition. In ICCV, 2013.
44. X. Yang and Y. Tian. Super normal vector for activity recognition using depth sequences. In CVPR, 2014.
45. M. Ye, Q. Zhang, L. Wang, J. Zhu, R. Yang, and J. Gall. A survey on human motion analysis from depth data. In Timeof-Flight and Depth Imaging. Sensors, Algorithms, and Applications. 2013.
46. G. Yu, Z. Liu, and J. Yuan. Discriminative orderlet mining for real-time recognition of human-object interaction. In ACCV, 2014.
47. J. Zhang, W. Li, P. O. Ogunbona, P. Wang, and C. Tang. Rgbd-based action recognition datasets: A survey. ArXiv, 2016.
48. Z. Zhang. Microsoft kinect sensor and its effect. IEEE MultiMedia, 2012.
49. W. Zhu, C. Lan, J. Xing, W. Zeng, Y. Li, L. Shen, and X. Xie. Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks. AAAI, 2016.