Iterative Template Matching Strategy for Visual Target Detection by Unmanned Surface Vehicle

  • Jin Xu
  • Ming Xie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9246)


The development of USV (Unmanned Surface Vehicle) has boomed around the world for military, research and commercial applications. The full autonomy of the USV is a desirable but challenging task. Though GPS is the main sensing system for the vehicle’s positioning and guidance, vision is necessary for tasks such as visual target detection and identification, especially color and shape encoded information. This is well demonstrated in the Maritime RobotX challenge 2014, where all of the five competition tasks require the use of vision to complete. The visual target detection for USV is a challenging task as the platform and target are always moving in the open sea area and the lighting condition varies a lot accordingly to weather and time. For real-time onboard performance, template matching is a good choice for the visual detection. In certain scenarios, the normal template matching method needs to be enhanced for robust performance. One of the example algorithm is the proposed iterative template matching, which provides a fast and robust solution for the vision tasks in the Maritime RobotX challenge 2014. By an additional step of searching for the visual context for the target, the robustness of detection is significantly improved without loss of accuracy.


Iterative template matching Visual target detection Unmanned surface vehicle 


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  1. 1.
    Caccia, M., Bibuli, M., Bono, R.: Basic navigation, guidance and control of an unmanned Surface Vehicle. Autonomous Robots 25, 349–365 (1994). Springer UsCrossRefGoogle Scholar
  2. 2.
    Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley, ISBN 978-0-470-51706-2 (2009)Google Scholar
  3. 3.
    Li, Y., Jian, L., Jinwen, T., Honbo, X.: A fast rotated template matching based on point feature. In: Proceedings of the SPIE 6043, pp. 453-459 (2005). MIPPR 2005: SAR and Multispectral Image ProcessingGoogle Scholar
  4. 4.
    Sirmacek, B., Unsalan, C.: Urban Area and Building Detection Using SIFT Keypoints and Graph Theory. IEEE Transactions on Geoscience and Remote Sensing 47(4), 1156–1167 (2009)CrossRefGoogle Scholar
  5. 5.
    Korman, S., Reichman, D., Tsur, G., Avidan, S.: FAsT-match: fast affine template matching. In: CVPR 2013 (2013)Google Scholar
  6. 6.
    Tamimoto, S.L.: Template Matching in Pyramids. Computer Graphics and Image Processing 16(4), 356–369 (1981)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Nanyang Technological UniversitySingapore CitySingapore
  2. 2.Nanjing Tech UniversityNanjing CityPeople’s Republic of China

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