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Fast background subtraction with adaptive block learning using expectation value suitable for real-time moving object detection

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Abstract

This paper presents a method of moving object detection through a fast background subtraction technique suitable for real-time performance in wide range of platforms. An intermittent background update using adaptive blocks individually calculates the learning rate through expected difference values. Then, coupled with a fast background subtraction process, the design achieves fast throughput with well-rounded performance. To compensate for the lagging effects of intermittent background update, an adaptation bias is devised to improve precision and recall metrics. Experiments show a versatile performance in varying scenes with overall results better than conventional techniques. The proposed method achieved a fast execution speed of up to 56 fps in PC using Full HD video. It also achieved 655 fps and 83 fps in PC and ARM core-embedded platform, respectively, using the minimum input resolution of 320 × 240. Overall, it is suitable for real-time performance applications.

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Acknowledgements

This work was supported by Kwangwoon University and by the MISP Korea under the National Program for Excellence in SW (2017-0-00096) supervised by IITP.

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Authors and Affiliations

  1. Department of Electronics and Communications Engineering, Kwangwoon University, Seoul, South Korea

    Vince Jebryl Montero, Woo-Young Jung & Yong-Jin Jeong

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  1. Vince Jebryl Montero
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  2. Woo-Young Jung
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Correspondence to Vince Jebryl Montero.

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Montero, V.J., Jung, WY. & Jeong, YJ. Fast background subtraction with adaptive block learning using expectation value suitable for real-time moving object detection. J Real-Time Image Proc 18, 967–981 (2021). https://doi.org/10.1007/s11554-020-01058-8

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