A hand tracking algorithm with particle filter and improved GVF snake model

Abstract

To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow (GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.

References

[1] KimS, BanY, LeeS. Sensors, 2017, 17: 166

[2] Abdul AzizMA, NiuJ, ZhaoX, LiX. IEEE Transactions on Cybernetics, 2016, 46: 945

[3] FanX, LiangD Q. Optoelectronics Letters, 2005, 1: 151

[4] WuD. Journal of Optoelectronics·Laser, 2015, 26: 960

[5] ZhaoS, SongX, TanW, LiHA Novel Approach to Hand Gesture Contour Detection Based on GVF Snake Model and Skin Color Elliptical ModelInternational Conference on Computer Application and System Modeling, 2010,

[6] WangF, ZhenY, ZhongB, JiR. Information Sciences, 2015, 301: 215

[7] XiaY, WuX Y, WangH Y. Journal of Optoelectronics ·Laser, 2012, 23: 2207

[8] GuanT, ZhouD, LiuY. IEEE Journal of Biomedical and Health Informatics, 2015, 19: 1494

[9] RathiY, VaswaniN, TannenbaumA, YezziA. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2007, 29: 1470

[10] Xu C, Yezzi A J and Prince J L, On the Relationship between Parametric and Geometric Active Contours, the 34th Asilomar Conference on Signals, Systems and Computers, 483 (2000).

[11] XiangY, ChungA C S, YeJ. Journal of Computational Physics, 2006, 219: 455

[12] Hess R and Fern A, Discriminatively Trained Particle Filters for Complex Multi-object Tracking, IEEE Conference on Computer Vision and Pattern Recognition, 240 (2009).

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