AIBM: Accurate and Instant Background Modeling for Moving Object Detection

Woo Jin Kim, Sangwon Hwang, Junhyeop Lee, Sungmin Woo, Sangyoun Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Detecting moving objects has been widely studied since it plays vital many applications, such as video surveillance and intelligent transportation systems. It is necessary to accurately differentiate the foreground and the background in this technology to analyze object motions in the scene. Conventional detection methods use many reference frames to model the background to detect moving objects; however, the detection is inaccurate when immediate changes occur in the scene because the instant update of the background model is impossible. To be robust illumination changes and dynamic backgrounds, we propose an accurate and instant background modeling (AIBM) method that inpaints the background with superpixels and enhances it in detail with pixel-levels. Unlike the previous approaches, the proposed AIBM method utilizes spatio-temporal information of only two consecutive frames to eliminate the lengthy initialization and update period of the model. In this paper, we illustrate the importance of accurate and instant background modeling in detecting moving objects. The performance of our method is evaluated with three benchmark datasets (CDnet2014, LASIESTA, and SBI). The experimental results show that our AIBM method is robust to sudden changes in the scene and outperforms the other conventional methods with F-measure of 0.8911 and 0.9682 in detection accuracy in CDNet2014 and LASIESTA datasets, respectively. The accuracy of the generated background is also measured on the SBI dataset, which demonstrates the importance of high-quality background modeling.

Original languageEnglish
Pages (from-to)9021-9036
Number of pages16
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number7
Publication statusPublished - 2022 Jul 1

Bibliographical note

Funding Information:
This work was supported by Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government, Ministry of Science and ICT (MSIT) under Grant 2016-0-00197 (Development of the high-precision natural 3D view generation technology using smart-car multi sensors and deep learning).

Publisher Copyright:
© 2000-2011 IEEE.

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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