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UT-MARO: Unscented Transformation and Matrix Rank Optimization for Moving Objects Detection in Aerial Imagery

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9474))

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Abstract

Aerial imagery is widely used in many civilian and military applications, as it provides a comprehensive view and real-time surveillance. Automated analysis is an essential task of aerial imagery to detect moving objects, however, the shakiness of these images and the small size of the moving objects are major challenges facing such task. This paper proposes UT-MARO, a novel moving object detection technique. UT-MARO achieves high accurate detection of small-size moving objects in shaky aerial images with low computation complexity and is composed of two phases: (1) UT-alignment and (2) MARO-extraction. UT-alignment utilizes unscented transformation to first align shaky images, then in the second phase, MARO-extraction detects small moving objects by extracting the background using low rank matrix optimization. The robustness of the proposed technique is tested on DARPA and UCF aerial images datasets; and the obtained results prove that UT-MARO has the best performance with lowest complexity compared to relevant current state of the art techniques.

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Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada

    Agwad ElTantawy & Mohamed S. Shehata

Authors
  1. Agwad ElTantawy
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  2. Mohamed S. Shehata
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Corresponding author

Correspondence to Agwad ElTantawy .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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ElTantawy, A., Shehata, M.S. (2015). UT-MARO: Unscented Transformation and Matrix Rank Optimization for Moving Objects Detection in Aerial Imagery. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_25

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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