Moving object detection for complex scenes by merging BG modeling and deep learning method

• Autorzy: Lin Chih-Yang , Huang Han-Yi , Lin Wei-Yang , Ng Hui-Fuang , Muchtar Kahlil , Nurdin Nadhila

Identyfikatory

DOI

10.2478/jaiscr-2023-0012

• Języki publikacji: EN

Abstrakty

EN

In recent years, many studies have attempted to use deep learning for moving object detection. Some research also combines object detection methods with traditional background modeling. However, this approach may run into some problems with parameter settings and weight imbalances. In order to solve the aforementioned problems, this paper proposes a new way to combine ViBe and Faster-RCNN for moving object detection. To be more specific, our approach is to confine the candidate boxes to only retain the area containing moving objects through traditional background modeling. Furthermore, in order to make the detection able to more accurately filter out the static object, the probability of each region proposal then being retained. In this paper, we compare four famous methods, namely GMM and ViBe for the traditional methods, and DeepBS and SFEN for the deep learning-based methods. The result of the experiment shows that the proposed method has the best overall performance score among all methods. The proposed method is also robust to the dynamic background and environmental changes and is able to separate stationary objects from moving objects. Especially the overall F-measure with the CDNET 2014 dataset (like in the dynamic background and intermittent object motion cases) was 0,8572.

Słowa kluczowe

EN

video surveillance; deep learning; moving object detection

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2023
• Tom: Vol. 13. No. 3
• Strony: 151--163
• Opis fizyczny: Biblior. 23 poz., rys.

Twórcy

autor

Lin Chih-Yang

• Department of Mechanical Engineering, National Central University, Taoyuan City 320317, Taiwan

• https://orcid.org/0000-0002-0401-8473

autor

Huang Han-Yi

• Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan

autor

Lin Wei-Yang

• Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan
• Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi 62102, Taiwan

• https://orcid.org/0000-0003-0895-2498

autor

Ng Hui-Fuang

• Department of Computer Science, University Tunku Abdul Rahman, Kampar 31900, Malaysia

• https://orcid.org/0000-0003-4394-2770

autor

Muchtar Kahlil

• Department of Electrical and Computer Engineering, Universitas Syiah Kuala Banda Aceh, Indonesia
• Telematics Research Center, Universitas Syiah Kuala Banda Aceh, Indonesia

• https://orcid.org/0000-0001-5740-1938

autor

Nurdin Nadhila

• Telematics Research Center, Universitas Syiah Kuala Banda Aceh, Indonesia

• https://orcid.org/0009-0008-0240-8639

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).