Abstract
This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow (underwater acoustic images) is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.
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This work was partly supported by the National Natural Science Foundation of China (Grant No. 51009040), Heilongjiang Postdoctoral Fund (Grant No. LBH-Z11205), the National High Technology Research and Development Program of China (863 Program, Grant No. 2011AA09A106), and the China Postdoctoral Science Foundation (Grant No. 2012M510928).
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Zhang, Td., Wan, L., Zeng, Wj. et al. Object detection and tracking method of AUV based on acoustic vision. China Ocean Eng 26, 623–636 (2012). https://doi.org/10.1007/s13344-012-0047-8
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DOI: https://doi.org/10.1007/s13344-012-0047-8