Zhong Zhang
ABSTRACT
For gesture and sign language recognition, hand shape and hand motion are the primary sources of information that differentiate one sign from another. Building an efficient and reliable hand detector is therefore an important step in recognizing signs and gestures. In this paper we evaluate three hand detection methods on three sign language data sets: a skin and motion detector [1], hand detection using multiple proposals [12], and chains model [9].
- J. Alon, V. Athitsos, Q. Yuan, and S. Sclaroff. A unified framework for gesture recognition and spatiotemporal gesture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9):1685--1699, 2009. Google Scholar
Digital Library
- V. Athitsos, J. Wang, S. Sclaroff, and M. Betke. Detecting instances of shape classes that exhibit variable structure. In European Conference on Computer Vision, pages 121--134, 2006. Google ScholarDigital Library
- P. Buehler, M. Everingham, D. P. Huttenlocher, and A. Zisserman. Long term arm and hand tracking for continuous sign language TV broadcasts. In British Machine Vision Conference, 2008.Google ScholarCross Ref
- P. Buehler, M. Everingham, D. P. Huttenlocher, and A. Zisserman. Upper body detection and tracking in extended signing sequences. International Journal of Computer Vision, 95(2):180--197, 2011. Google ScholarDigital Library
- N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 886--893, June 2005. Google ScholarDigital Library
- A. Farhadi, D. A. Forsyth, and R. White. Transfer learning in sign language. In IEEE Conference on Computer Vision and Pattern Recognition, 2007.Google ScholarCross Ref
- C. Helen and B. Richard. Large lexicon detection of sign language. In IEEE international conference on Human-computer interaction, pages 88--97, 2007. Google ScholarDigital Library
- M. Jones and J. Rehg. Statistical color models with application to skin detection. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 1063--6919, Jun 1999.Google ScholarCross Ref
- L. Karlinsky, M. Dinerstein, D. Harari, and S. Ullman. The chains model for detecting parts by their context. In IEEE International Conference on Computer Vision and Pattern Recognition, pages 25--32, 2010.Google ScholarCross Ref
- M. Kölsch and M. Turk. Robust hand detection. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 614--619, 2004. Google ScholarDigital Library
- M. P. Kumar, A. Zisserman, and P. H. S. Torr. Efficient discriminative learning of parts-based models. In IEEE International Conference on Computer Vision, pages 552--559, 2009.Google ScholarCross Ref
- A. Mittal, A. Zisserman, and P. H. S. Torr. Hand detection using multiple proposals. In British Machine Vision Conference, 2011.Google ScholarCross Ref
- V. I. Morariu, D. Harwood, and L. S. Davis. Tracking people's hands and feet using mixed network and/or search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(5):1248--1262, May 2013. Google ScholarDigital Library
- E. J. Ong and R. Bowden. A boosted classifier tree for hand shape detection. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 889--894, 2004. Google ScholarDigital Library
- T. Pfister, J. Charles, M. Everingham, and A. Zisserman. Automatic and efficient long term arm and hand tracking for continuous sign language tv broadcasts. In British Machine Vision Conference, 2012.Google ScholarCross Ref
- A. Thayananthan, B. Stenger, P. H. S. Torr, and R. Cipolla. Shape context and chamfer matching in cluttered scenes. In IEEE Conference on Computer Vision and Pattern Recognition, pages 127--133, 2003. Google ScholarDigital Library
- H. Trinh, Q. Fan, P. Gabbur, and S. Pankanti. Hand tracking by binary quadratic programming and its application to retail activity recognition. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1902--1909, 2012. Google ScholarDigital Library
- P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 511--518, 2001.Google ScholarCross Ref
- Q. Yuan, S. Sclaroff, and V. Athitsos. Automatic 2d hand tracking in video sequences. In IEEE Workshop on Applications of Computer Vision, pages 250--256, 2005. Google ScholarDigital Library
- Z. Zhong, A. Rommel, and A. Vassilis. Experiments with computer vision methods for hand detection. In Proceedings of the 4th International Conference on Pervasive Technologies Related to Assistive Environments, pages 21:1--21:6, 2011. Google ScholarDigital Library
Index Terms
- Hand detection on sign language videos
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