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http://dx.doi.org/10.5573/ieie.2014.51.3.164

A Framework of Recognition and Tracking for Underwater Objects based on Sonar Images : Part 2. Design and Implementation of Realtime Framework using Probabilistic Candidate Selection  

Lee, Yeongjun (Korea Institute of Ocean Science & Technology)
Kim, Tae Gyun (Korea Institute of Ocean Science & Technology)
Lee, Jihong (Dept. of Mechanical Engineering, Chungnam National University)
Choi, Hyun-Taek (Korea Institute of Ocean Science & Technology)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.51, no.3, 2014 , pp. 164-173 More about this Journal
Abstract
In underwater robotics, vision would be a key element for recognition in underwater environments. However, due to turbidity an underwater optical camera is rarely available. An underwater imaging sonar, as an alternative, delivers low quality sonar images which are not stable and accurate enough to find out natural objects by image processing. For this, artificial landmarks based on the characteristics of ultrasonic waves and their recognition method by a shape matrix transformation were proposed and were proven in Part 1. But, this is not working properly in undulating and dynamically noisy sea-bottom. To solve this, we propose a framework providing a selection phase of likelihood candidates, a selection phase for final candidates, recognition phase and tracking phase in sequence images, where a particle filter based selection mechanism to eliminate fake candidates and a mean shift based tracking algorithm are also proposed. All 4 steps are running in parallel and real-time processing. The proposed framework is flexible to add and to modify internal algorithms. A pool test and sea trial are carried out to prove the performance, and detail analysis of experimental results are done. Information is obtained from tracking phase such as relative distance, bearing will be expected to be used for control and navigation of underwater robots.
Keywords
Framework; Underwater Object Recognition; Multiple Candiate; Probability; Imaging sonar;
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Times Cited By KSCI : 1  (Citation Analysis)
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