Abstract
This paper proposed a Gaussian mixture model based gradient level set method (GMM-GLS) for moving target contour tracking in video sequences to handle automatic initialization and background variation. In contrast with conventional level set models, adaptive GMM background subtraction is applied to get the rough location of moving target as foreground in current frame. And more accurate mask image according to the rough location of foreground with dilatation operation indicates the initialization contour of level set evolution. Then, the gradient level set model can evolve the curve quickly and ensure more accurate convergence to the target contour in tracking procedure. Based on this accurate mask, the GMM-GLS method can greatly reduce the uncertain iteration time in curve convergence and optimize the initialization of GLS eliminating the interferential background. Experimental results on many real-world video sequences validate that our approach greatly improves the performance of object contour tracking.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. Int J Comput Vis 1:321–331. doi:10.1007/bf00133570
Xu C, Prince J (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7:359–369. doi:10.1109/83.661186
Caselles V, Catte F, Coll T, Dibos F (1993) A geometric model for active contours in image processing. Numer Math 66:1–31. doi:10.1007/BF01385685
Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22:61–79. doi:10.1023/A:1007979827043
Osher S, Sethian JA (1988) Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulation. J Comput Phys 79:12–49. doi:10.1016/0021-9991(88)90002-2
Weickert J, Romeny BMH, Viergever MA (1998) Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans Image Process 7:398–410. doi:10.1109/83.661190
Li C, Xu C, Gui C (2005) Level set evolution without re-initialization: a new variational formulation. Proc IEEE Conf Comput Vis Pattern Recogn 1:430–436. doi:10.1109/CVPR.2005.213
Mumford D, Shah J (1989) Optimal approximation by piecewise smooth functions and associated variational problems: commun. Comm Pure Appl Math 42:577–685. doi:10.1002/cpa.3160420503
Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10:266–277. doi:10.1109/83.902291
Rathi Y, Vaswani N, Tannenbaum A, Yezzi A (2005) Particle filtering for geometric active contours with application to tracking moving and deforming objects. IEEE Comput Soc Conf Comput Vis Pattern Recogn 2:2–9. doi:10.1109/CVPR.2005.271
Sun X, Yao H, Zhang S (2011) A novel supervised level set method for non-rigid object tracking. IEEE Conf Digital Object Identifier Comput Vis Pattern Recogn 3393–3400. doi: 10.1109/CVPR.2011.5995656
Zivkovic Z (2004) Improved adaptive Gaussian mixture model for background subtraction. Proceedings of the 17th international conference on pattern recognition, vol 2, pp 23–26. doi: 10.1109/ICPR.2004.1333992
The School of Informatics of the University of Edinburgh (2004) CAVIAR Test Case Scenarios. http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1. Accessed 31 Apr 2013
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Y., Lu, X., Zhu, M. (2014). A Fast Active Contour Tracking Method Based on Gaussian Mixture Model. In: Zhang, B., Mu, J., Wang, W., Liang, Q., Pi, Y. (eds) The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-00536-2_116
Download citation
DOI: https://doi.org/10.1007/978-3-319-00536-2_116
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00535-5
Online ISBN: 978-3-319-00536-2
eBook Packages: EngineeringEngineering (R0)