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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*
(Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, 201306, PR China)
Shengzheng Wang
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, PR China)
Chaojian Shi
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, PR China)
Huafeng Wu
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, PR China)
Jiansen Zhao
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, PR China)
Junjie Fu
(Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, PR China)


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.

Research Article
Copyright © The Royal Institute of Navigation 2018 

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