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Multi-scale ship tracking via random projections

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Abstract

Ship tracking plays a key role in inland waterway closed circuit television (CCTV) video surveillance. Although much success has been demonstrated in the construction of effective appearance model, numerous issues remain to be addressed due to factors such as pose and illumination change, partial or full occlusion, abrupt scale variation and motion blur. In this paper, we firstly inherit the intrinsical merits of subspace representation which demonstrates robustness to partial or full occlusion, pose and illumination variation. A very sparse measurement matrix is adopted to extract the features for the appearance model. A naive Bayes classifier with online update is employed to determine whether the image patch belongs to the foreground or background. Secondly, in order to increase the randomness of the random projection matrix and further reduce memory load, we develop our ship appearance model based on fern features in the compressed domain. Thirdly, we track the scale by enhancing the tracker with a mechanism of feedback. Finally, both qualitative and quantitative evaluations on numerous challenging CCTV videos demonstrate that the proposed algorithm achieves favorable performance in terms of efficiency and accuracy.

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Acknowledgments

The authors would like to thank the editor and reviewers for their valuable comments and suggestions that lead to an improved manuscript. This work is supported by the National Science Foundation of China (NSFC 51279152).

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Authors and Affiliations

  1. School of Automation, Wuhan University of Technology, Wuhan, 430070, China

    Fei Teng & Qing Liu

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  1. Fei Teng
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  2. Qing Liu
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Correspondence to Fei Teng.

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Teng, F., Liu, Q. Multi-scale ship tracking via random projections. SIViP 8, 1069–1076 (2014). https://doi.org/10.1007/s11760-014-0629-4

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