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Robust Ship Tracking via Multi-view Learning and Sparse Representation

Published online by Cambridge University Press:  13 September 2018

Xinqiang Chen ,
Shengzheng Wang ,
Chaojian Shi ,
Huafeng Wu ,
Jiansen Zhao  and
Junjie Fu
Xinqiang Chen*
Affiliation:
(Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, 201306, PR China)
Shengzheng Wang
Affiliation:
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, PR China)
Chaojian Shi
Affiliation:
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, PR China)
Huafeng Wu
Affiliation:
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, PR China)
Jiansen Zhao
Affiliation:
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, PR China)
Junjie Fu
Affiliation:
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, PR China)
*
(E-mail: [email protected])
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Abstract

Conventional visual ship tracking methods employ single and shallow features for the ship tracking task, which may fail when a ship presents a different appearance and shape in maritime surveillance videos. To overcome this difficulty, we propose to employ a multi-view learning algorithm to extract a highly coupled and robust ship descriptor from multiple distinct ship feature sets. First, we explore multiple distinct ship feature sets consisting of a Laplacian-of-Gaussian (LoG) descriptor, a Local Binary Patterns (LBP) descriptor, a Gabor filter, a Histogram of Oriented Gradients (HOG) descriptor and a Canny descriptor, which present geometry structure, texture and contour information, and more. Then, we propose a framework for integrating a multi-view learning algorithm and a sparse representation method to track ships efficiently and effectively. Finally, our framework is evaluated in four typical maritime surveillance scenarios. The experimental results show that the proposed framework outperforms the conventional and typical ship tracking methods.

Keywords

Ship trackingMulti-view learningSparse representationSmart ship
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 1 , January 2019 , pp. 176 - 192
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

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