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http://dx.doi.org/10.7746/jkros.2020.15.1.008

Study on Underwater Object Tracking Based on Real-Time Recurrent Regression Networks Using Multi-beam Sonar Images  

Lee, Eon-ho (Mechanical Engineering, Kongju National University)
Lee, Yeongjun (Korea Research Institute of Ships and Ocean Engineering)
Choi, Jinwoo (Korea Research Institute of Ships and Ocean Engineering)
Lee, Sejin (Division of Mechanical & Automotive Engineering, Kongju National University)
Publication Information
The Journal of Korea Robotics Society / v.15, no.1, 2020 , pp. 8-15 More about this Journal
Abstract
This research is a case study of underwater object tracking based on real-time recurrent regression networks (Re3). Re3 has the concept of generic object tracking. Because of these characteristics, it is very effective to apply this model to unclear underwater sonar images. The model also an pursues object tracking method, thus it solves the problem of calculating load that may be limited when object detection models are used, unlike the tracking models. The model is also highly intuitive, so it has excellent continuity of tracking even if the object being tracked temporarily becomes partially occluded or faded. There are 4 types of the dataset using multi-beam sonar images: including (a) dummy object floated at the testbed; (b) dummy object settled at the bottom of the sea; (c) tire object settled at the bottom of the testbed; (d) multi-objects settled at the bottom of the testbed. For this study, the experiments were conducted to obtain underwater sonar images from the sea and underwater testbed, and the validity of using noisy underwater sonar images was tested to be able to track objects robustly.
Keywords
Underwater Sonar Image; Object Tracking; Real-Time Recurrent Regression Networks; Heterogeneous Sonar Sensors;
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Times Cited By KSCI : 2  (Citation Analysis)
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