Underwater Target Tracking Algorithm Based on an Improved Color Matching

Article Preview

Abstract:

Owing to fuzzy detail and distortion of underwater image and complex changes of target, the underwater target tracking system requires accuracy and continuity of tracking, and expects that the size of tracking window can adapt to appearance change of target. According to the requirements mentioned above, the underwater target tracking algorithm based on an improved color matching is proposed, which finds the best location of target through tracking accuracy algorithm and calculates width of window on the basis of tracking window size variation algorithm. The experimental results show that this algorithm can adaptively track the real-time target and has higher accuracy than traditional color matching algorithm.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 834-836)

Pages:

1234-1239

Citation:

Online since:

October 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] XU Yuru,XIAO Kun. Technology development of autonomous ocean vehicle [J] . Acta Automatics Sinca, 2007,33( 5) : 518-521.

Google Scholar

[2] Stauffer C,Grimson W.E.L. Learning patterns of activity using real-time tracking. IEEE Trans PAM. 2000,22(8):747-757.

DOI: 10.1109/34.868677

Google Scholar

[3] Hager G,Dewan M,Stewart C. Multiple Kernel Tracking with SSD [J]. Proc of IEEE Conf on Computer Vision and pattern Recognition. 2004,(l): 790-797.

DOI: 10.1109/cvpr.2004.1315112

Google Scholar

[4] Comaniciu D , Ramesh V , Meer P. Kernel- based object tracking[J]. IEEE Transaction on pattern analysis and machine intelligence, 2003, 25 (5) :564 -577.

DOI: 10.1109/tpami.2003.1195991

Google Scholar

[5] Comaniciu D , Ramesh V , Meer P. Real-Time tracking of non- rigid objects using mean shift [A] . Proc IEEE Conference on Computer Vision and Pattern Recognition[C] . 2000: 142-149.

DOI: 10.1109/cvpr.2000.854761

Google Scholar

[6] Zhaowen Wang, XiaoKang Yang, Yi Xu. Cam-Shift guided particle filter for visual tracking[C]. Signal Processing Systems. IEEE Workshop, 2007:301- 306.

DOI: 10.1109/sips.2007.4387562

Google Scholar

[7] Zheng Zhang, YanPing Wang, GuiXiang Xue. Digital image processing and machine vision (In Chinese). People Post Press. (2010).

Google Scholar

[8] Collins R. Mean- shift blob tracking through scale space [C]. IEEE Conference Computer Vision and Pattern Recognition, 2003, 2: 234- 240.

DOI: 10.1109/cvpr.2003.1211475

Google Scholar