Abstract
In this study, we present a novel multi-cues active contours based method for tracking target contours using edge, region, and shape information. To locate the target position, a contour based meanshift tracker is designed which combines both color and texture information. In order to reduce the adverse impact of sophisticated background and accelerate the curve motion, we extract rough target region from the coming frame by the proposed target appearance model. What’s more, both discriminative pre-learning based global layer and voting based local layer are integrated into our appearance model. For obtaining the detailed target boundaries, we embed edge, region, and shape information into the level sets based multi-cues active contour model (MCAC). Experiments on seven video sequences demonstrate that the proposed method performs better than other competitive contour tracking methods under various tracking environment.
P. Lv—This work is supported by the National Natural Science Foundation of China (No. 61175096 and No. 61273273) and Specialized Fund for Joint Building Program of Beijing Municipal Education Commission.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yilmaz, A.: Kernel-based object tracking using asymmetric kernels with adaptive scale and orientation selection. Mach. Vision Appl. 22(2), 255–268 (2011)
Paragios, N., Deriche, R.: Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 22(3), 266–280 (2000)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. In: Proceedings of IEEE International Conference on Computer Vision, pp. 694–699 (1995)
Zhang, T., Freedman, D.: Improving performance of distribution tracking through background. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 282–287 (2005)
Niethammer, M., Tannenbaum, A., Angenent, S.: Dynamic active contours for visual tracking. IEEE Trans. Autom. Control 51(4), 562–579 (2006)
Bibby, C., Reid, I.: Real-time tracking of multiple occluding objects using level sets. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 1307–1314 (2010)
Vaswani, N., Rathi, Y., Yezzi, A., Tannenbaum, A.: Deform pf-mt: particle filter with mode tracker for tracking nonaffine contour deformations. IEEE Trans. Image Process. 19(4), 841–857 (2010)
Cai, L., He, L., Yamashita, T., Yiren, X., Zhao, Y., Yang, X.: Robust contour tracking by combining region and boundary information. IEEE Trans. Circuits Syst. Video Technol. 21(12), 1784–1794 (2011)
Fan, J., Shen, X., Ying, W.: Scribble tracker: a matting-based approach for robust tracking. IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1633–1644 (2012)
Chen, L., Zhou, Y., Wang, Y., Yang, J.: GACV: geodesic-aided c-v method. Pattern Recogn. 39(7), 1391–1395 (2006)
Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lv, P., Zhao, Q. (2015). Tracking Deformable Target via Multi-cues Active Contours. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-24075-6_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24074-9
Online ISBN: 978-3-319-24075-6
eBook Packages: Computer ScienceComputer Science (R0)