DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed with Deep Features

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

Volumn 2016-December, Issue , 2016, Pages 4150-4158

  Sinha, Ayan ^a   Choi, Chiho ^a   Ramani, Karthik ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ACTIVATION; COMPUTER VISION; MOTION ESTIMATION; NEURAL NETWORKS; PATTERN RECOGNITION;

CONVOLUTIONAL NEURAL NETWORK; HAND POSE ESTIMATIONS; HIERARCHICAL ESTIMATION; LOW DIMENSIONAL; MATRIX COMPLETION; NEAREST NEIGHBORS; REAL TIME PERFORMANCE; STATE-OF-THE-ART METHODS;

PARAMETER ESTIMATION;

EID: 84986266731     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.450     Document Type: Conference Paper

References (31)

1. C. Choi, A. Sinha, J. H. Choi, S. Jang, and K. Ramani. A collaborative filtering approach to real-time hand pose estimation. In Computer Vision (ICCV), 2015 IEEE International Conference on.
2. D. Ciresan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 3642-3649. IEEE, 2012.
3. W. S. Cleveland and S. J. Devlin. Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403):596-610, 1988.
4. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. In Proceedings of The 31st International Conference on Machine Learning, pages 647-655, 2014.
5. A. Erol, G. Bebis, M. Nicolescu, R. D. Boyle, and X. Twombly. Vision-based hand pose estimation: A review. Computer Vision and Image Understanding, 108(1):52-73, 2007.
6. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580-587, 2014.
7. H. Jégou, F. Perronnin, M. Douze, J. Sanchez, P. Perez, and C. Schmid. Aggregating local image descriptors into compact codes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(9):1704-1716, 2012.
8. S. Ji, W. Xu, M. Yang, and K. Yu. 3d convolutional neural networks for human action recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(1):221-231, 2013.
9. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei. Large-scale video classification with convolutional neural networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 1725-1732, 2014.
10. C. Keskin, F. K?rac?, Y. E. Kara, and L. Akarun. Hand pose estimation and hand shape classification using multi-layered randomized decision forests. In Computer Vision-ECCV 2012, pages 852-863. Springer, 2012.
11. C. Keskin, F. K?rac?, Y. E. Kara, and L. Akarun. Real time hand pose estimation using depth sensors. In Consumer Depth Cameras for Computer Vision, pages 119-137. Springer, 2013.
12. M. J. Landau, B. Y. Choo, and P. A. Beling. Simulating kinect infrared and depth images. 2015.
13. J. Lee and T. L. Kunii. Model-based analysis of hand posture. Computer Graphics and Applications, IEEE, 15(5):77-86, 1995.
14. S. Melax, L. Keselman, and S. Orsten. Dynamics based 3d skeletal hand tracking. In Proceedings of Graphics Interface 2013, pages 63-70. Canadian Information Processing Society, 2013.
15. M. Oberweger, P. Wohlhart, and V. Lepetit. Hands deep in deep learning for hand pose estimation. arXiv preprint arXiv:1502.06807, 2015.
16. I. Oikonomidis, N. Kyriazis, and A. A. Argyros. Efficient model-based 3d tracking of hand articulations using kinect. In BMVC, volume 1, page 3, 2011.
17. C. Qian, X. Sun, Y. Wei, X. Tang, and J. Sun. Realtime and robust hand tracking from depth. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 1106-1113. IEEE, 2014.
18. T. Sharp, C. Keskin, D. Robertson, J. Taylor, J. Shotton, D. K. C. R. I. Leichter, A. V. Y. Wei, D. F. P. K. E. Krupka, A. Fitzgibbon, and S. Izadi. Accurate, robust, and flexible real-time hand tracking. In Proc. CHI, volume 8, 2015.
19. J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio, A. Blake, M. Cook, and R. Moore. Real-time human pose recognition in parts from single depth images. Communications of the ACM, 56(1):116-124, 2013.
20. S. Sridhar, F. Mueller, A. Oulasvirta, and C. Theobalt. Fast and robust hand tracking using detection-guided optimization. In Proceedings of Computer Vision and Pattern Recognition (CVPR), 2015.
21. S. Sridhar, A. Oulasvirta, and C. Theobalt. Interactive markerless articulated hand motion tracking using rgb and depth data. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Dec. 2013.
22. S. Sridhar, H. Rhodin, H.-P. Seidel, A. Oulasvirta, and C. Theobalt. Real-time hand tracking using a sum of anisotropic Gaussians model. In Proceedings of the International Conference on 3D Vision (3DV), Dec. 2014.
23. X. Sun, Y.Wei, S. Liang, X. Tang, and J. Sun. Cascaded hand pose regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 824-832, 2015.
24. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1-9, 2015.
25. A. Tagliasacchi, M. Schroeder, A. Tkach, S. Bouaziz, M. Botsch, and M. Pauly. Robust articulated-icp for realtime hand tracking. Technical report, 2015.
26. D. Tang, H. J. Chang, A. Tejani, and T.-K. Kim. Latent regression forest: Structured estimation of 3d articulated hand posture. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 3786-3793. IEEE, 2014.
27. D. Tang, T.-H. Yu, and T.-K. Kim. Real-time articulated hand pose estimation using semi-supervised transductive regression forests. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 3224-3231. IEEE, 2013.
28. A. Thayananthan, R. Navaratnam, B. Stenger, P. H. Torr, and R. Cipolla. Multivariate relevance vector machines for tracking. Springer, 2006.
29. J. Tompson, M. Stein, Y. LeCun, and K. Perlin. Real-time continuous pose recovery of human hands using convolutional networks. ACM Transactions on Graphics (TOG), 33(5):169, 2014.
30. C. Xu and L. Cheng. Efficient hand pose estimation from a single depth image. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 3456-3462. IEEE, 2013.
31. 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, pages 149-187. Springer, 2013.