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.

Abstract
1. 서 론
2. 관련 연구
3. 실시간 순환 신경망
    3.1 실시간 순환 신경망 구조
4. 실험 결과
    4.1 부유체 테스트 베드 실험
    4.2 침전체 해안 실험
    4.3 침전체 테스트 베드 실험
    4.4 다양한 침전체 테스트 베드 실험
5. 결 론
References

• 이언호(Mechanical Engineering, Kongju National University) | Eon-ho Lee
• 이영준(Korea Research Institute of Ships and Ocean Engineering) | Yeongjun Lee
• 최진우(Korea Research Institute of Ships and Ocean Engineering) | Jinwoo Choi
• 이세진(Division of Mechanical & Automotive Engineering, Kongju National University) | Sejin Lee Corresponding author