The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
권호 표지
DOI QR코드

DOI QR Code

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

실시간 순환 신경망 기반의 멀티빔 소나 이미지를 이용한 수중 물체의 추적에 관한 연구

  • 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)
  • Received : 2019.11.29
  • Accepted : 2020.01.28
  • Published : 2020.02.28
Download PDF
⟨ Previous Next ⟩

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

References

  1. A. Seth, Your brain hallucinates your conscious reality, TED2017, [Online], https://www.ted.com/talks/anil_seth_your_brain_hallucinates_your_conscious_reality/transcript?language=ko, Accessed: April 26, 2017.
  2. M. Kristan et al., "The visual object tracking VOT2014 challenge results," Eur. Conf. Comput. Vision Workshops, pp. 191-217, 2014.
  3. M. Kristan et al., "The visual object tracking VOT2015 challenge results," 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), Santiago, Chile, pp. 1-23, 2015.
  4. M. Kristan et al., "The visual object tracking VOT2016 challenge results," Eur. Conf. Comput. Vision Workshops, pp. 777-823, 2016.
  5. M. Kristan et al., "The visual object tracking VOT2017 challenge results," 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 1949-1972, 2017.
  6. M. Danelljan, G. Hager, F. S. Khan, and M. Felsberg, "Discriminative scale space tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 8, pp. 1561-1575, August, 2017. https://doi.org/10.1109/TPAMI.2016.2609928
  7. K.Kang et al., "T-CNN: Tubelets with convolutional neural networks for object detection from videos," IEEE Trans. Circuits Syst. Video Technol., vol. 28, no. 10, pp. 2896-2907, 2018. https://doi.org/10.1109/TCSVT.2017.2736553
  8. H. Nam and B. Han, "Learning multi-domain convolutional neural networks for visual tracking," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4293-4302, 2016.
  9. D. Gordon, A. Farhadi, and D. Fox., "Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects," IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 788-795, April, 2018. https://doi.org/10.1109/LRA.2018.2792152
  10. S. Lee, "Deep Learning of Submerged Body Images from 2D Sonar Sensor based on Convolutional Neural Network," 2017 IEEE Underwater Technology (UT), pp. 1-3, 2017.
  11. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Communications of the ACM, pp. 84-90, June, 2017.
  12. S. Lee, B. Park, and A. Kim, "Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection," arXiv:1810.07990 [cs.RO], October, 2018.
  13. E.-H. Lee, Y. Lee, J. Choi, and S. Lee, "Study of Marker Detection Performance on Deep Learning via Distortion and Rotation Augmentation of Training Data on Underwater Sonar Image," Journal of Korea Robotics Society, vol. 14, no. 1, pp. 14-21, March, 2019. https://doi.org/10.7746/jkros.2019.14.1.014
  14. S. R en, K. He, R . Gir shick, a nd J. Sun, " Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, June, 2017. https://doi.org/10.1109/TPAMI.2016.2577031
  15. K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, "LSTM: A search space odyssey," IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 10, pp. 2222-2232, October, 2017. https://doi.org/10.1109/TNNLS.2016.2582924