Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3239576.3239601acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicaipConference Proceedingsconference-collections
research-article

Survey on Vision Based Positioning of Dim-small Targets in the Sea-sky Background

Published: 16 June 2018 Publication History

Abstract

How to improve the performance of dim-small target positioning algorithm under the sea-sky background is a hot and urgent problem in military and civil fields. In this regard, this paper analyzes the feature of dim-small targets, compares the performance of various non-visual positioning methods for localization of dim-small targets in the context of sea-sky, and draws the necessity of visual positioning methods in practical applications; Then, a comprehensive introduction is presented to current visual orientation. In the mainstream methods, three kinds of popular simultaneous localization and mapping (SLAM) systems are compared for simulation experiments. A visual positioning method is applied to the actual maritime positioning experiment. According to the experimental results, the difficulties and key technologies of the visual positioning of dim-small targets in the sea-sky are analyzed. Finally, in order to improve single-sensor positioning performance, the development and trend of multi-source information fusion in the field of target location are introduced.

References

[1]
Chang, K. K. 2016. Research on Binocular Vision Positioning Method about Maritime Targets in Shipborne Video. Master's thesis. Dalian Maritime University.
[2]
Huang, X. Y. 2017. Infrared Small Target Detection Under Long Distance Sea-Sky Background. Master's thesis. University of Electronic Science and Technology of China.
[3]
Marr, D. 1982. Vision: A computational investigation into the human representation and processing of visual information. Henry Holt and Co., Inc. New York, NY, USA.
[4]
Mouaddib, E., Batle, J., and Salvi, J. 1997. Recent Progress in structured light in order to solve the correspondence problem in stereo vision. Proceedings of the 1997 IEEE International Conference on Robotics and Automation (New Mexico, USA, April 20 - 25, 1997). ICRA '97. IEEE, Los Alamitos, California, 130--136.
[5]
Tech, E. K. and Mital, D. P. 1995. A transputer-based automated visual inspection system for electronic devices and PCBs. Optics and Lasers in Engineering. 22, 3 (Dec. 1995), 161--180.
[6]
Zhang, Y. and Kovacevic, R. 1997. Real-time sensing of sag geometry during GTA welding. Journal of Manufacturing Science and Engineering. 119, 2 (1997), 151--159.
[7]
Abdel-Aziz, Y. I. and Karara, H. M. 1971. Direct linear transformation into object space coordinates in close-range photogrammetry. Proceedings of the Symposium on Close-Range Photogrammetry. Falls Church, VA, USA. (1971), 1--18.
[8]
Ganapathy, S. 1984. Decomposition of transformation matrices for robot vision. IEEE International Conference on Robotics and Automation (Atlanta, GA, USA, March 13 - 15). ICRA '84. IEEE, Los Alamitos, California, 130--139.
[9]
Tsai, R. Y. 1986. An efficient and accurate camera calibration technique for 3D machine vision. IEEE Conference on Computer Vision and Pattern Recognition (Miami, FL, USA, June 22 - 26). CVPR '86. IEEE, Los Alamitos, California, 364--374.
[10]
Zhang, Z. Y. 2000. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence. 22, 11 (Nov. 2000), 1330--1334.
[11]
Longuet-Higgins, H. C. 1981. A computer algorithm for reconstructing a scene from two projections. Nature. 293, 10 (Sep. 1981), 133--135.
[12]
Faugeras, O. D. and Maybank, S. 1990. Motion from point matches: multiplicity of solutions. Intl. Computer Vision. 4, 3 (1990), 225--246.
[13]
Fischler, M. A. and Bolles, R. C. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM. 24, 6 (Jun. 1981), 381--395.
[14]
Horn, B. 1970. Shape from shading: a method for obtaining the shape of a smooth opaque object from one view. Doctoral Thesis. Massachusetts Institute of Technology Cambridge.
[15]
Woodham, R. J. 1980. Photometric method for determining surface orientation from multiple images. Optical Engineering. 19, 1 (Jan. 1980), 139--144.
[16]
Wiktin, A. 1981. Recovering surface shape and orientation from texture. Artificial Intelligence. 17, 45 (Aug. 1981), 17--45.
[17]
Martin, W. N. and Aggarwal, J. K. 1983. Volumetric descriptions of objects from multiple views. IEEE Trans on PAMI. 5, 2 (Feb. 1983), 150--158.
[18]
Akimoto, T., Suenaga, Y., and Wallace, R. 1993. Automatic creation of 3D facial models. IEEE Computer Graphics & Apageslications. 13, 5 (Sep. 1993), 16--22.
[19]
Stewart, C. V. and Dyer C. R. 1988. The trinocular general support algorithm: a three-camera stereo algrithm for overcoming binocular matching errors. Proceedings of the 2nd International Conference on Computer Vision (Tampa, Florida, USA, December 05 - 08, 1988). ICCV '88. IEEE, Los Alamitos, California, 134--138.
[20]
Yin, Y. S. 2014. Research on Weak Small Target Detection on the Sea Based on Panoramic Vision. Master's thesis. Harbin Engineering University.
[21]
Zeng, W. J., Wan, L., and Zhang, T. D. et al. 2012. Fast Detection of Sea Line Based on the Visible Characteristics of Marine Images. Acta Optica Sinica. In Chinese. 32, 1 (Jan. 2012), 82--89.
[22]
Wang, Z. H., Liu, J. G., and Deng, H. 2017. Small-target infrared image processing based on novel weighted-local entropy. J. Huazhong Univ. of Sci. & Tech. (Natural Science Edition). In Chinese. 45, 8 (Aug. 2017), 42--46.
[23]
Yin, H. J. 2013. Research of imaging clearly and area constant of Target in motion in the field of view of the zoom lens. Master's thesis. University of Chinese Academy of Sciences.
[24]
Li, C. 2015. Research on Key Technologies in Multi-Level Processing for Electronic Reconnaissance of Radar. Doctoral Thesis. National University of Defense Technology.
[25]
Richard, P. H. 2017. Underwater Acoustics-Analysis, Design and Performance of Sonar. China Ocean Press, Beijing.
[26]
Reinhard, N. 2017. Laser Measurement Technology: Fundamentals and Applications. Huazhong University of Science and Technology Press, Wuhan, Hubei.
[27]
Kenneth, R. B. 2017. Inertial Navigation Systems Analysis. National Defend Industry Press, Beijing.
[28]
Lepetit, V., Moreno-Noguer, F., and Fua, P. 2009. EPnP: An Accurate O(n) Solution to the PnP Problem. International Journal of Computer Vision. 81, 2 (Feb. 2009), 155--166.
[29]
Yang, S. and Wu, F. C. 2011. Weighted linear methods for the camera pose estimation. Journal of Software. 22, 10 (Oct. 2011), 2476--2487.
[30]
Chen, P. and Hu, G. D. 2012. A Simple Algorithm for Camera Pose Estimation. In Proceedings of International Conference on Mechatronics and Automation (Chengdu, China, August 05 - 08, 2012). ICMA '12. IEEE, Los Alamitos, California, 2181--2186.
[31]
Forstner, W. 2010. Minimal representations for uncertainty and estimation in projective spaces. In Proceedings of the Asian Conference on Computer Vision (Queenstown, New Zealand, November 08 - 12, 2010). ACCV '10. AFCV, 619--632.
[32]
Ferraz, L., Binefa, X., and Moreno, N. 2014. Very fast solution to the PnP problem with algebraic outlier rejection. IEEE Conference on Computer Vision and Pattern Recognition (Columbus, Ohio, USA, June 24 - 27, 2014). CVPR '14. IEEE, Los Alamitos, California, 501--508.
[33]
Li, S. Q., Xu, C., and Xie, M. 2012. A Robust O(n) Solution to the Perspective-n-Point Problem. Transactions on Pattern Analysis and Machine Intelligence. 34, 7 (Jan. 2012), 1444--1450.
[34]
Zheng, Y., Kuang, Y., and Sugimoto, S. et al. 2013. Revisiting the PnP Problem: A Fast, General and Optimal Solution. IEEE International Conference on Computer Vision (Sydney, Australia, December 01 - 08, 2013). ICCV '13. IEEE, Los Alamitos, California, 2344--2351.
[35]
Liu, C., Zhu, F., and Ou, J. J. 2012. Monocular Pose Determination from Three Perpendicular Lines. PR & AI. In Chinese. 25, 5 (Oct. 2012), 737--744.
[36]
Liu, C., Zhu, F., and Ou, J. J. et al. 2012. Closed form solutions and properties of Z shaped P3L problem. PR & AI. In Chinese. 33, 12 (Dec. 2012), 2714--2720.
[37]
Jun, W. U. 2012. An Improved Tsai's Two-stage Camera Calibration Approach Using Vanish Point Constrain. Geomatics & Information Science of Wuhan University. In Chinese. 37, 1 (Jan. 2012), 17--21.
[38]
Zhao, H., Shao, S. L., and Li, D. Z. et al. 2013. Four-Rotor Aircraft's Autonomous Navigation in Corridor Based on Vanish Point. Journal of Northeastern University. In Chinese. 34, 11 (Mar. 2013), 1546--1549.
[39]
Feng, C. 2013. Research on Identification and Location of Object Based on Monocular Vision. Doctoral Thesis. Nanjing University of Aeronautics and Astronautics.
[40]
Miao, X. K., Zhu, F., and Ding, Q. H. et al. 2013. Monocular Vision Pose Measurement Based on Docking Ring Component. Acta Optica Sinica. In Chinese 33, 4 (Apr. 2013), 115--123.
[41]
Wu, B., Ye, D., and Guo, Y. B. 2017. Multiple Circle Recognition and Pose Estimation for Aerospace Applications. Acta Optica Sinica. In Chinese. 37, 9 (Sep. 2017), 115--123.
[42]
Yang, L. 2015. Research on Methods for Dense Scene Reconstruction by Monocular Moving Camera. Doctoral Thesis. University of Chinese Academy of Sciences.
[43]
Wu, C. 2013. Towards Linear-Time Incremental Structure from Motion. 2013 International Conference on 3D Vision (Seattle, Washington, USA, June 29 - July 1). 3DV'13. IEEE, Los Alamitos, California, 127--134.
[44]
Snavely, N., Seitz, S. M., and Szeliski, R. 2006. Photo tourism: exploring photo collections in 3D. Acm Transactions on Graphics. 25, 3 (Jan. 2006), 835--846.
[45]
Suavely, N., Seitz, S. M., and Szeliski, R. 2008. Modeling the World from Internet Photo Collections. International Journal of Computer Vision. 80, 2 (Nov. 2008), 189--210.
[46]
Agarwal, S., Suavely, N., and Simon, I. 2009. Building Rome in a Day. IEEE International Conference on Computer Vision (Kyoto, Japan, September 27 - October 04). ICCV '09. IEEE, Los Alamitos, California, 72--79.
[47]
Liu, H. M., Zhang, G. F., and Bao, H. J. 2016. A Survey of Monocular Simultaneous Localization and Mapping. Journal of Computer-Aided Design & Computer Graphics. In Chinese. 28, 6 (Jun. 2016), 855--868.
[48]
Klein, G. and Murray, D. 2007. Parallel tracking and mapping for small AR workspaces. In Proceedings of IEEE and ACM International Symposium on Mixed and Augmented Reality (Nara, Japan, November 13 - 16). ISMAR '07. IEEE, Los Alamitos, California, 225--234.
[49]
Klein, G. and Murray, D. 2009. Parallel tracking and mapping on a camera phone. Proceedings of IEEE and ACM International Symposium on Mixed and Augmented Reality (Orlando, Florida, USA, October 19 - 22). ISMAR '09. IEEE, Los Alamitos, California, 83--86.
[50]
Mur-Artal, R., Montiel, J. M. M., and Tardos, J. D. 2015. ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Transactions on Robotics. 31, 5 (Oct. 2015), 1147--1163.
[51]
Mur-Artal, R. and Tardos, J. D. 2016. ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. IEEE Transactions on Robotics. 33, 5 (Oct. 2016), 1255--1262.
[52]
Engel, J., Sturm, J., and Cremers, D. 2013. Semi-dense visual odometry for a monocular camera. In Proceedings of IEEE International Conference on Computer Vision (Sydney, Australia, December 01 - 08). ICCV '13. IEEE, Los Alamitos, California, 1449--1456.
[53]
Engel, J., Schöps, T., and Cremers, D. 2014. LSD-SLAM: large-scale direct monocular SLAM. In Proceedings of European Conference on Computer Vision (Zurich, Switzerland, September 06 - 12). ECCV '14.834--849.
[54]
Ono, K., Ogawa, T., and Maeda, Y. et al. 2013. Recognition and Bin-Picking of Coil Springs by Stereo Vision. Transactions of the Japan Society of Mechanical Engineers. In Japanese. 79, 804 (Aug. 2013), 2769--2779.
[55]
Li, H., Chen, Y. L., and Chang, T. et al. 2011. Binocular vision positioning for robot grasping. 2011 IEEE International Conference on Robotics and Biomimetics (Phuket Island, Thailand, December 07 - 11). ROBIO '11. IEEE, Los Alamitos, California, 1522--1527.
[56]
Premebida, C., Carreira, J., and Batista, J. et al. 2014. Pedestrian detection combining RGB and dense LIDAR data. 2014 IEEE/RSJ International Conference on Intelligent Robot and Systems (Chicago, Illinois, USA, September 14 - 18). IROS '14. IEEE, Los Alamitos, California, 4112--4117.
[57]
Shan, Q., Adams, R., and Curless, B. et al. 2013. The Visual Turing Test for Scene Reconstruction. 2013 International Conference on 3D Vision (Seattle, Washington, USA, June 29 - July 1). 3DV'13. IEEE, Los Alamitos, California, 25--32.
[58]
Liu, H., Wei, Z. Q., and Li, Z. et al. 2015. The Research and Implementation of a Multi-Camera System. Periodical of Ocean University of China. In Chinese. 45, 3 (Mar. 2015), 128--135.
[59]
Yang, D. W., Liu, Z. G., and Zhang, J. S. et al. 2014. A novel time-domain constrained approach for the initial alignment of INS/vision integrated system. Chinese Journal of Scientific Instrument. In Chinese. 35, 4 (Apr. 2014), 788--793.
[60]
Sirtkaya, S., Seymens, B., and Alatan, A. A. 2013. Loosely coupled Kalman filtering for fusion of Visual Odometry and inertial navigation. 2013 16th International Conference on Proceedings of the Information Fusion (Istanbul, Turkey, July 9 - 12). FUSION '13. IEEE, Los Alamitos, California, 219--226.
[61]
Perlin, V., Johnson, D., and Rohde, M. et al. 2011. Fusion of visual odometry and inertial data for enhanced real-time egomotion estimation. SPIE Defense, Security, and Sensing (Orlando, Florida, USA, April 25 - 29). SPIE DSS '11. SPIE, 2155--2157.
[62]
Wang, F., Cui, J. Q., and Chen, B. W. et al. 2013. A Comprehensive UAV Indoor Navigation SystemBased on Vision Optical Flow and Laser FastSLAM. Acta Automatica Sinica. 39, 11 (Nov. 2013), 1889--1900.
[63]
Broggi, A., Cerri, P., Ghidoni, S., Grisleri, P., and Jung, H.G.J.H.G. 2009. A New Approach to Urban Pedestrian Detection for Automatic Braking. IEEE Transactions on Intelligent Transportation Systems 2009. 10, 4 (Oct. 2009), 594--605.
[64]
Li, F. G. 2014. Study on Ranging Sonar-optical Vision Target Positioning and UUV Control Technology. Master's thesis. Harbin Engineering University.
[65]
Ivaeanan, S. and Trivedi, M. M. 2011. Combining monocular and stereo-vision for real-time vehicle ranging and tracking on multiliane highways. 14th International IEEE Conference on Intelligent Transportation Systems (Washington, DC, USA, October 5 - 7). ITSC '11. IEEE, New York, NY, 1249--1254.
[66]
Lei, Y. Z., Zhao, H. J., and Jiang, H. Z. 2008. A Three-Dimensional Measurement Method by Combining Binocular and Monocular Vision Systems. Acta Optica Sinica. In Chinese. 27, 7 (Jul. 2008), 1338--1342.
[67]
Bilodeau, G. A., Torabi, A., and Morin, F. 2011. Visible and Infrared Image Registration Using Trajectories and Composite Foreground Images. Image and Vision Computing. 29, 1 (Jan. 2011), 41--50.
[68]
Zhao, G. P., Bo, Y. M., and Yin, M. F. et al. 2012. An Object Tracking Method Based on Infrared and Visible Dual-channel Video. Journal of Electronics & Information Technology. In Chinese. 34, 3 (Mar. 2012), 529--534.

Index Terms

  1. Survey on Vision Based Positioning of Dim-small Targets in the Sea-sky Background

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICAIP '18: Proceedings of the 2nd International Conference on Advances in Image Processing
    June 2018
    261 pages
    ISBN:9781450364607
    DOI:10.1145/3239576
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    • University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
    • Southwest Jiaotong University

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 June 2018

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. SLAM
    2. dim-small targets
    3. information fusion
    4. sea-sky background
    5. visual positioning

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • the Special Fund for the Taishan Scholar Project

    Conference

    ICAIP '18

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 79
      Total Downloads
    • Downloads (Last 12 months)2
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 04 Oct 2024

    Other Metrics

    Citations

    View Options

    Get Access

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media