Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection

IEEE Transactions on Image Processing

Volumn 26, Issue 8, 2017, Pages 3911-3920

  Chang, Xiaojun ^a   Ma, Zhigang ^a   Lin, Ming ^b   Yang, Yi ^c   Hauptmann, Alexander G ^a  

^a Carnegie Mellon University ^*  (United States)

^b UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^c UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

fast kinect motion detection; Feature interaction augmented sparse learning

Indexed keywords

FEATURE EXTRACTION; MOTION ANALYSIS;

CLASSIFICATION MODELS; FEATURE INTERACTIONS; HIGHER ORDER EFFECTS; LINEAR ALGORITHMS; LINEAR PROPERTIES; MOTION DETECTION; NONLINEAR EFFECT; OBJECTIVE FUNCTIONS;

HUMAN COMPUTER INTERACTION;

EID: 85028434870     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2017.2708506     Document Type: Article

References (35)

1. J. Pansiot, A. Elsaify, B. Lo, and G.-Z. Yang, "Racket: Real-time autonomous computation of kinematic elements in tennis," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, Sep./Oct. 2009, pp. 773-779.
2. Y. Jin, X. Hu, and G. Wu, "A tai chi training system based on fast skeleton matching algorithm," in Proc. Eur. Conf. Comput. Vis. Workshops Demonstrations, 2012, pp. 667-670.
3. X. Chang, Y.-L. Yu, Y. Yang, and E. P. Xing, "Semantic pooling for complex event analysis in untrimmed videos," IEEE Trans. Pattern Anal. Mach. Intell., to be published. [Online]. Available: https://doi.org/10.1109/TPAMI.2016.2608901
4. X. Chang, Z. Ma, Y. Yang, Z. Zeng, and A. G. Hauptmann, "Bi-level semantic representation analysis for multimedia event detection," IEEE Trans. Cybern., vol. 47, no. 5, pp. 1180-1197, May 2017.
5. X. Chang and Y. Yang, "Semisupervised feature analysis by mining correlations among multiple tasks," IEEE Trans. Neural Netw. Learn. Syst., to be published. [Online]. Available: https://doi.org/10.1109/TNNLS.2016.2582746
6. R. T. Trevor Hastie and J. Friedman, The Elements of Statistical Learning, vol. 2. Springer, 2009.
7. D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis, vol. 821. Hoboken, NJ, USA: Wiley, 2012.
8. Y. Liu, J. Wang, and J. Ye, "An efficient algorithm for weak hierarchical lasso," in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2014, pp. 283-292.
9. P. Radchenko and G. M. James, "Variable selection using adaptive nonlinear interaction structures in high dimensions," J. Amer. Statist. Assoc., vol. 105, no. 492, pp. 1541-1553, 2010.
10. P. Gong, C. Zhang, Z. Lu, J. Z. Huang, and J. Ye, "A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems," in Proc. 30th Int. Conf. Mach. Learn., Atlanta, GA, USA, Jun. 2013, pp. 37-45.
11. K. Lin, J. Xu, I. M. Baytas, S. Ji, and J. Zhou, "Multi-task feature interaction learning," in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, San Francisco, CA, USA, Aug. 2016, pp. 1735-1744.
12. N. C. Tang, Y.-Y. Lin, J.-H. Hua, S.-E. Wei, M.-F. Weng, and H.-Y. M. Liao, "Robust action recognition via borrowing information across video modalities," IEEE Trans. Image Process., vol. 24, no. 2, pp. 709-723, Feb. 2015.
13. G. Chen, D. Clarke, M. Giuliani, A. Gaschler, and A. Knoll, "Combining unsupervised learning and discrimination for 3D action recognition," Signal Process., vol. 110, pp. 67-81, May 2015.
14. R. Slama, H. Wannous, M. Daoudi, and A. Srivastava, "Accurate 3D action recognition using learning on the Grassmann manifold," Pattern Recognit., vol. 48, no. 2, pp. 556-567, 2015.
15. Y. Du, W. Wang, and L. Wang, "Hierarchical recurrent neural network for skeleton based action recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Boston, MA, USA, Jun. 2015, pp. 1110-1118.
16. T. Batabyal, T. Chattopadhyay, and D. P. Mukherjee, "Action recognition using joint coordinates of 3D skeleton data," in Proc. IEEE Int. Conf. Image Process. (ICIP), Quebec City, QC, Canada, Sep. 2015, pp. 4107-4111.
17. B. B. Amor, J. Su, and A. Srivastava, "Action recognition using rateinvariant analysis of skeletal shape trajectories," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 1, pp. 1-13, Jan. 2016.
18. A. Shahroudy, T.-T. Ng, Q. Yang, and G. Wang, "Multimodal multipart learning for action recognition in depth videos," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 10, pp. 2123-2129, Oct. 2016.
19. F. Liang, Z. Zhang, X. Li, and Z. Tong, "Lower limb action recognition with motion data of a human joint," Arabian J. Sci. Eng., vol. 41, no. 12, pp. 5111-5121, 2016.
20. L. Zhou, W. Li, and P. Ogunbona, "Learning a pose lexicon for semantic action recognition," in Proc. IEEE Int. Conf. Multimedia Expo (ICME), Seattle, WA, USA, Jul. 2016, pp. 1-6.
21. Y. Hou, Z. Li, P. Wang, and W. Li, "Skeleton optical spectra based action recognition using convolutional neural networks," IEEE Trans. Circuits Syst. Video Technol., to be published. [Online]. Available: https://doi.org/10.1109/TCSVT.2016.2628339
22. C. Wang, Y. Wang, and A. L. Yuille, "Mining 3D key-pose-motifs for action recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 2639-2647.
23. G. Obozinski, B. Taskar, and M. I. Jordan, "Joint covariate selection and joint subspace selection for multiple classification problems," Statist. Comput., vol. 20, no. 2, pp. 231-252, Apr. 2010.
24. F. Nie, H. Huang, and C. H. Q. Ding, "Low-rank matrix recovery via efficient schatten p-norm minimization," in Proc. AAAI Conf. Artif. Intell., Jul. 2012, pp. 655-661.
25. M. Zinkevich, "Online convex programming and generalized infinitesimal gradient ascent," in Proc. Int. Conf. Mach. Learn., 2003, pp. 928-936.
26. V. Bloom, D. Makris, and V. Argyriou, "G3D: A gaming action dataset and real time action recognition evaluation framework," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, Jun. 2012, pp. 7-12.
27. 3DLife/Huawei ACM MM Grand Challenge, accessed on 2011. [Online]. Available: http://perso.telecom-paristech.fr/~essid/3dlife-gc-11/
28. F. Negin, F. Özdemir, C. B. Akgül, K. A. Yüksel, and A. Erçil, "A decision forest based feature selection framework for action recognition from RGB-depth cameras," in Proc. Image Anal. Recognit., 2013, pp. 648-657.
29. C. Chen, Y. Zhuang, F. Nie, Y. Yang, F. Wu, and J. Xiao, "Learning a 3D human pose distance metric from geometric pose descriptor," IEEE Trans. Vis. Comput. Graphics, vol. 17, no. 11, pp. 1676-1689, Nov. 2011.
30. R. Garg and R. Khandekar, "Gradient descent with sparsification: An iterative algorithm for sparse recovery with restricted isometry property," in Proc. Int. Conf. Mach. Learn., Jun. 2009, pp. 337-344.
31. P. Loh and M. J. Wainwright, "Regularized m-estimators with nonconvexity: Statistical and algorithmic theory for local optima," in Proc. Conf. Adv. Neural Inf. Process. Syst., 2013, pp. 476-484.
32. L. Xiao and T. Zhang, "A proximal-gradient homotopy method for the sparse least-squares problem," SIAM J. Optim., vol. 23, no. 2, pp. 1062-1091, 2013.
33. A. A. Halim, C. Dartigues-Pallez, F. Precioso, M. Riveill, A. Benslimane, and S. Ghoneim, "Human action recognition based on 3D skeleton part-based pose estimation and temporal multi-resolution analysis," in Proc. ICIP, Sep. 2016, pp. 3041-3045.
34. D. K. Pal and O. J. Mengshoel, "Stochastic CoSaMP: Randomizing greedy pursuit for sparse signal recovery," in Proc. ECML PKDD, 2016, pp. 761-776.
35. W. Zhang, L. Zhang, R. Jin, D. Cai, and X. He, "Accelerated sparse linear regression via random projection," in Proc. 13th AAAI Conf. Artif. Intell., Phoenix, AZ, USA, Feb. 2016, pp. 2337-2343.