수중 로봇을 위한 다중 템플릿 및 가중치 상관 계수 기반의 물체 인식 및 추종
Multiple Templates and Weighted Correlation Coefficient-based Object Detection and Tracking for Underwater Robots
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초록
The camera has limitations of poor visibility in underwater environment due to the limited light source and medium noise of the environment. However, its usefulness in close range has been proved in many studies, especially for navigation. Thus, in this paper, vision-based object detection and tracking techniques using artificial objects for underwater robots have been studied. We employed template matching and mean shift algorithms for the object detection and tracking methods. Also, we propose the weighted correlation coefficient of adaptive threshold -based and color-region-aided approaches to enhance the object detection performance in various illumination conditions. The color information is incorporated into the template matched area and the features of the template are used to robustly calculate correlation coefficients. And the objects are recognized using multi-template matching approach. Finally, the water basin experiments have been conducted to demonstrate the performance of the proposed techniques using an underwater robot platform yShark made by KORDI.
Abstract
The camera has limitations of poor visibility in underwater environment due to the limited light source and medium noise of the environment. However, its usefulness in close range has been proved in many studies, especially for navigation. Thus, in this paper, vision-based object detection and tracking techniques using artificial objects for underwater robots have been studied. We employed template matching and mean shift algorithms for the object detection and tracking methods. Also, we propose the weighted correlation coefficient of adaptive threshold -based and color-region-aided approaches to enhance the object detection performance in various illumination conditions. The color information is incorporated into the template matched area and the features of the template are used to robustly calculate correlation coefficients. And the objects are recognized using multi-template matching approach. Finally, the water basin experiments have been conducted to demonstrate the performance of the proposed techniques using an underwater robot platform yShark made by KORDI.
참고문헌 (18)
- D. Feezor, "Autonomous Underwater Vehicle Homing", Docking via Electromagnetic Guidance, 2001, pp.26, 515~521
- P. Corke, "Experiments with Underwater Robot Localization and Tracking", Proc. of International Conf. on Robotics and Automation, pp.4556~4561 (2007)
- P. Corke, "Autonomous Underwater Vehicle Navigation", IEEE Journal of Oceanic Engineering, 35(3), pp.663~678 (2010)
- S. C. Yu, "Navigation of Autonomous Underwater Vehiclesbased on Articial Underwater Landmarks", Proc.of MTS, 2001, pp.1, 409~416
- J. Sattar, "Robust Servo-Control for Underwater Robots using Banks of Visual Filters", Proc. of International Conference on Robotics and Automation, pp.3585~3588 (2009)
- G. Dudek, "A Visually Guided Swimming Robot", Proc. of IEEE, 2005, 3604~3609
- P. M. Lee, "Visual Servoing for Underwater Docking of an Autonomous Underwater Vehicle with One Camera", Proc. of Oceans, 2, pp.677~682 (2003)
- A. Negre, "Robust Vision-based Underwater TargetIdentication and Homing Using Self-SimilarLandmarks", Journal of Field Robotics, 25, pp.360~377 (2008)
- F. Maire, "A Vision Based Target Detection System for Docking of an Autonomous Underwater Vehicle", Proc. of Australasian Conf. on Robotics and Automation (2009)
- 한경민, "수중로봇을 위한 형태를 기반으로 하는 인공표식의 인식 및 추종 알고리즘", 전자공학회논문지 - SC, 48(6), pp.91~98 (2011), 대한전자공학회
- J. P. Lewis, "Fast Template Matching", Vision Interface, pp.120~123 (1995)
- D. Comaniciu, "Mean shift: A robust approach toward feature space analysis", IEEE Trans. Pattern Anal. and Machine Intell, 24(5) (2002)
- K. Han, "지능형 수중로봇의 연구동향과 수중 영상처리 소개", 전자공학회지, 38(7), pp.37~41 (2011)
- Z. Zhang, "A Flexible New Technique for Camera Calibration", IEEE Trans. on Pattern Anal. and Machine Intell, 22, pp.1330~1334 (2000)
- D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints", International Journal on Computer Vision, 60(2), pp.91~110 (2004)
- H. Bay, "SURF: Speeded Up Robust Features", European Conf. on Computer Vision, 3951, pp.404~417 (2006)
- D. Comaniciu, "Kernel-Based Object Tracking", IEEE Trans. PatternAnal. and Machine Intell, 25(5), pp.564~577 (2003)
- "Video of Vision-based Underwater Navigation"[URL]