Abstract
Hand detection is a challenging task in hand gesture recognition system and the detection results can be easily affected by changes in hand shapes, viewpoints, lightings or complex backgrounds. In order to detect and localize the human hands in static images against complex backgrounds, a hand detection method based on a mixture of multi-scale deformable part models is proposed in this paper, which is trained discriminatively using latent SVM and consists of three components each defined by a root filter and three part filters. The hands are detected in a feature pyramid in which the features are variants of HOG descriptors. The experimental results show that the proposed method is invariant to small deformations of hand gestures and the mixture model has a good performance on NUS hand gesture dataset - II.
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References
Zhu, Y., Yang, Z., Yuan, B.: Vision based hand gesture recognition. In: 2013 International Conference on Service Sciences, pp. 260–265. IEEE Press, Shenzhen (2013)
Yu, C., Wang, X., Huang, H., Shen, J.: Vision-based hand gesture recognition using combinational features. In: 2010 6th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 543–546. IEEE Press, Darmstadt (2010)
Pisharady, P.K., Vadakkepat, P., Loh, A.P.: Attention based detection and recognition of hand postures against complex backgrounds. Int. J. Comput. Vis. 101, 403–419 (2013)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893. IEEE Press, San Diego (2005)
Dadgostar, F., Sarrafzadeh, A., Messom, C.: Multi-layered hand and face tracking for real-time gesture recognition. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008, Part I. LNCS, vol. 5506, pp. 587–594. Springer, Heidelberg (2009)
Dardas, N.H., Georganas, N.D.: Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans. Instrum. Meas. 60(11), 3592–3607 (2011)
Dardas, N.H., Petriu, E.M.: Hand Gesture detection and recognition using principal component analysis. In: 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, pp. 1–6. IEEE Press, Ottawa (2011)
Viola, P., Jones, M.J.: Robust real-time object detection. Int. J. Comput. Vis. 2(57), 137–154 (2004)
Stergiopoulou, E., Sgouropoulos, K., Nikolaou, N., Papamarkos, N.: Real time hand detection in a complex background. Eng. Appl. Artif. Intell. 35, 54–70 (2014)
Fang, Y., Wang k., Cheng J., Lu, H., C.: A real-time hand gesture recognition method. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 995–998. IEEE Press, Beijing (2007)
Ong, E.J., Bowden, R.: A boosted classifier tree for hand shape detection. In: 6th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 889–894. IEEE Press, Jeju Island (2006)
Wu, Y., Huang, T.S.: View-independent recognition of hand postures. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 88–94. IEEE Press, Hilton Head Island (2000)
Zondag, J.A., Gritti, T., Jeanne, V.: Practical study on real-time hand detection. In: 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–8. IEEE Press, Amsterdam (2009)
Liew, C.F., Yairi, T.: Generalized BRIEF: a novel fast feature extraction method for robust hand detection. In: 2014 22nd International Conference on Pattern Recognition, pp. 3014–3019. IEEE Press, Stockholm (2014)
Mittal, A., Zisserman, A., Torr, P.H.S.: Hand detection using multiple proposals. In: 2011 British Machine Vision Conference, pp. 75.1–75.11. BMVA Press, Scotland (2011)
NUS Hand Posture Datasets. https://www.ece.nus.edu.sg/stfpage/elepv/NUS-HandSet
Zhang, J.G., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis. 73(2), 213–238 (2007)
Felzenszwalb, P.F., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE Press, Anchorage (2008)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Felzenszwalb, P.F., Huttenlocher D.: Distance transforms of sampled functions. Technical report 2004-1963, CIS, Cornell University (2004)
Acknowledgements
This research was supported by the National Natural Science Foundation of China under grant No. 61370134, the National High Technology Research and Development Program of China (863 Program) under grant No. 2013AA013904.
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Zou, C., Liu, Y., Wang, J., Si, H. (2016). Deformable Part Model Based Hand Detection against Complex Backgrounds. In: Tan, T., et al. Advances in Image and Graphics Technologies. IGTA 2016. Communications in Computer and Information Science, vol 634. Springer, Singapore. https://doi.org/10.1007/978-981-10-2260-9_17
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DOI: https://doi.org/10.1007/978-981-10-2260-9_17
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