3D action recognition using multi-temporal depth motion maps and fisher vector

IJCAI International Joint Conference on Artificial Intelligence

Volumn 2016-January, Issue , 2016, Pages 3331-3337

  Chen, Chen ^a   Liu, Mengyuan ^b   Zhang, Baochang ^c   Han, Jungong ^d   Junjun, Jiang ^e   Liu, Hong ^b  

^a The University of Texas at Dallas   (United States)

^b Peking University   (China)

^c BEIHANG UNIVERSITY   (China)

^d NORTHUMBRIA UNIVERSITY   (United Kingdom)

^e CHINA UNIVERSITY OF GEOSCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

ACTION RECOGNITION; EXTREME LEARNING MACHINE; LOCAL BINARY PATTERNS; ROTATION INVARIANT; SHAPE DISCRIMINATION; SPEED VARIATIONS; STATE-OF-THE-ART APPROACH; TEXTURE INFORMATION;

LEARNING SYSTEMS;

EID: 85006085657     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (36)

1. J. K. Aggarwal and Xia Lu. Human activity recognition from 3d data: A review. PRL, 48 (1): 70-80, 2014.
2. Victoria Bloom, Dimitrios Makris, and Vasileios Argyriou. G3d: A gaming action dataset and real time action recognition evaluation framework. In CVPRW, pages 7-12, 2012.
3. Chen Chen, Kui Liu, and Nasser Kehtarnavaz. Real-time human action recognition based on depth motion maps. Journal of Real-Time Image Processing, pages 1-9, 2013.
4. Chen Chen, Nasser Kehtarnavaz, and Roozbeh Jafari. A medication adherence monitoring system for pill bottlesbased on a wearable inertial sensor. In EMBC, pages 4983-4986, 2014.
5. Chen Chen, Kui Liu, Roozbeh Jafari, and Nasser Kehtarnavaz. Home-based senior fitness test measurement system using collaborative inertial and depth sensors. In EMBC, pages 4135-4138, 2014.
6. Chen Chen, R. Jafari, and N. Kehtarnavaz. Action recognition from depth sequences using depth motion mapsbased local binary patterns. In WACV, pages 1092-1099, 2015.
7. Chen Chen, Baochang Zhang, Zhenjie Hou, Junjun Jiang, Mengyuan Liu, and Yun Yang. Action recognition from depth sequences using weighted fusion of 2d and 3d auto-correlation of gradients features. Multimedia Tools and Applications, pages 1-19, 2016.
8. Yong Du, WeiWang, and LiangWang. Hierarchical recurrent neural network for skeleton based action recognition. In CVPR, pages 1110-1118, 2015.
9. Georgios Evangelidis, Gurkirt Singh, and Radu Horaud. Skeletal quads: human action recognition using joint quadruples. In ICPR, pages 4513-4518, 2014.
10. Z. Gao, H. Zhang, G. P. Xu, and Y. B. Xue. Multi-perspective and multi-modality joint representation and recognition model for 3d action recognition. Neurocomputing, 151: 554-564, 2015.
11. Lena Gorelick, Moshe Blank, Eli Shechtman, Michal Irani, and Ronen Basri. Actions as space-time shapes. TPMAI, 29 (12): 2247-2253, 2007.
12. Guang-Bin Huang, Qin-Yu Zhu, and Chee-Kheong Siew. Extreme learning machine: Theory and applications. Neurocomputing, 70 (1): 489-501, 2006.
13. Yu Kong, B. Satarboroujeni, and Yun Fu. Hierarchical 3d kernel descriptors for action recognition using depth sequences. In FG, pages 1-6, 2015.
14. Wanqing Li, Zhengyou Zhang, and Zicheng Liu. Action recognition based on a bag of 3d points. In CVPRW, pages 9-14, 2010.
15. Yan-Ching Lin, Min-Chun Hu, Wen-Huang Cheng, Yung-Huan Hsieh, and Hong-Ming Chen. Human action recognition and retrieval using sole depth information. In ACM MM, pages 1053-1056, 2012.
16. Hong Liu, Lu Tian, Mengyuan Liu, and Hao Tang. Sdm-bsm: A fusing depth scheme for human action recognition. In ICIP, pages 4674-4678, 2015.
17. Cewu Lu, Jiaya Jia, and Chi Keung Tang. Rangesample depth feature for action recognition. In CVPR, pages 772-779, 2014.
18. Bingbing Ni, Gang Wang, and P. Moulin. Rgbdhudaact: A color-depth video database for human daily activity recognition. In ICCVW, pages 1147-1153, 2011.
19. Eshed Ohn-Bar and Mohan M. Trivedi. Joint angles similiarities and hog2 for action recognition. In CVPRW, pages 465-470, 2013.
20. Timo Ojala, Matti Pietikäinen, and Topi Mäenpää. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. TPMAI, 24 (7): 971-987, 2002.
21. O. Oreifej and Zicheng Liu. Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences. In CVPR, pages 716-723, 2013.
22. Florent Perronnin, Jorge Sanchez, and Thomas Mensink. Improving the fisher kernel for large-scale image classification. In ECCV, pages 143-156, 2010.
23. Hossein Rahmani, Arif Mahmood, Q Huynh Du, and Ajmal Mian. HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition. Springer International Publishing, 2014.
24. Hossein Rahmani, Arif Mahmood, Du Q Huynh, and Ajmal Mian. Real time action recognition using histograms of depth gradients and random decision forests. In WACV, pages 626-633, 2014.
25. Hossein Rahmani, Q. Huynh Du, Arif Mahmood, and Ajmal Mian. Discriminative human action classification using locality-constrained linear coding. PRL, 2015.
26. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake. Real-time human pose recognition in parts from single depth images. In CVPR, pages 1297-1304, 2011.
27. Shuai Tang, Xiaoyu Wang, Xutao Lv, Tony X. Han, James Keller, Zhihai He, Marjorie Skubic, and Shihong Lao. Histogram of Oriented Normal Vectors for Object Recognition with a Depth Sensor. Springer Berlin Heidelberg, 2013.
28. Raviteja Vemulapalli, Felipe Arrate, and Rama Chellappa. Human action recognition by representing 3d human skeletons as points in a lie group. In CVPR, pages 588-595, 2014.
29. Heng Wang and Cordelia Schmid. Action recognition with improved trajectories. In ICCV, pages 3551-3558, 2013.
30. Jiang Wang, Zicheng Liu, Jan Chorowski, Zhuoyuan Chen, and YingWu. Robust 3d action recognition with random occupancy patterns. In ECCV, pages 872-885, 2012.
31. Jiang Wang, Zicheng Liu, Ying Wu, and Junsong Yuan. Learning actionlet ensemble for 3D human action recognition. TPAMI, 36 (5): 914-927, 2014.
32. Lu Xia and J. K. Aggarwal. Spatiotemporal depth cuboid similarity feature for activity recognition using depth camera. In CVPR, pages 2834-2841, 2013.
33. Xiaodong Yang and YingLi Tian. Super normal vector for activity recognition using depth sequences. In CVPR, pages 804-811, 2014.
34. Xiaodong Yang, Chenyang Zhang, and Ying Li Tian. Recognizing actions using depth motion maps-based histograms of oriented gradients. ACM MM, pages 1057-1060, 2012.
35. Mihai Zanfir, Marius Leordeanu, and Cristian Sminchisescu. The moving pose: An efficient 3d kinematics descriptor for low-latency action recognition and detection. In ICCV, pages 2752-2759, 2013.
36. Chenyang Zhang and Yingli Tian. Histogram of 3D facets: A depth descriptor for human action and hand gesture recognition. CVIU, 139, 2015.