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Moving Object Detection for a Moving Camera Based on Global Motion Compensation and Adaptive Background Model

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  • Intelligent Control and Applications
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Abstract

A fast and effective moving object detection method for a moving camera is proposed in this paper. The global motion is estimated through tracking the grid-based key points using optical flow. After the motion compensation, the background model, candidate background model and candidate age are used for the background modelling. Then the local pixel difference and the consistency of local changes between the current frame and the background model are used for the background subtraction. The lighting influence threshold and the local pixel difference between the current frame and two previous aligned frames are used to reduce the lighting influences. Finally, Gaussian filter, connected-components analysis, erosion and dilation are used to refine the results. The performance evaluation shows that this proposed method works very fast in real time and has competitive results compared with others in the public dataset.

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Authors and Affiliations

  1. School of Electrical Engineering, University of Ulsan, Daehak road 93, Namgu, Ulsan, 44610, Korea

    Yang Yu, Laksono Kurnianggoro & Kang-Hyun Jo

Authors
  1. Yang Yu
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  2. Laksono Kurnianggoro
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  3. Kang-Hyun Jo
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Corresponding author

Correspondence to Kang-Hyun Jo.

Additional information

Recommended by Associate Editor Dong-Joong Kang under the direction of Editor Euntai Kim. This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ICT Consilience Creative program(IITP-2019-2016-0-00318) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation).

Yang Yu received the B.S. degree in automation from Northeastern University, China, in 2002. He received the M.S. degree in control theory and control engineering from Liaoning University of Technology, China, in 2006. He is currently with Liaoning University of Technology as an associate professor. He is currently a Ph.D. candidate at the Graduate School of Electrical Engineering, University of Ulsan, Ulsan, Korea. His research interests include intelligent control and computer vision.

Laksono Kurnianggoro received his bachelor of engineering from the University of Gadjah Mada, Indonesia, in 2010. He is currently a Ph.D. student at the Graduate School of Electrical Engineering, University of Ulsan, Ulsan, Korea. He is actively participating as a member of societies such as IEEE. His research interest include stereo vision, 3D image processing, computer vision, and machine learning. He has scientific publications in some publishers such as IEEE, Springer, and Elsevier. He also involved in several projects including development of autonomous vehicle system, advanced car washer system, low-cost 3D scanner, autonomous robot, and many mores. He is also an active contributor of the popular computer vision library, like OpenCV.

Kang-Hyun Jo received the Ph.D. degree in Computer Controlled Machinery from Osaka University, Japan, in 1997. After a year of experience at ETRI as a postdoctoral research fellow, he joined the School of Electrical Engineering, University of Ulsan, Ulsan, Korea. He has served as a director or an AdCom member of Institute of Control, Robotics and Systems, the Society of Instrument and Control Engineers, and IEEE IES Technical Committee on Human Factors Chair. Currently, he is serving as AdCom member, and from 2018, as the Secretary, of the IEEE IES. He has also been involved in organizing many international conferences such as International Workshop on Frontiers of Computer Vision, International Conference on Intelligent Computation, International Conference on Industrial Technology, International Conference on Human System Interactions, and Annual Conference of the IEEE IES. At present, he is an Editorial Board Member for international journals, such as the International Journal of Control, Automation, and Systems and the Transactions on Computational Collective Intelligence. His research interests include computer vision, robotics, autonomous vehicle, and ambient intelligence.

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Yu, Y., Kurnianggoro, L. & Jo, KH. Moving Object Detection for a Moving Camera Based on Global Motion Compensation and Adaptive Background Model. Int. J. Control Autom. Syst. 17, 1866–1874 (2019). https://doi.org/10.1007/s12555-018-0234-3

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  • DOI: https://doi.org/10.1007/s12555-018-0234-3

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