<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 <italic>F</italic>-measure with the CDNET 2014 dataset (like in the dynamic background and intermittent object motion cases) was 0,8572.</p>
</abstract>

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.

Moving Object Detection for Complex Scenes by Merging BG Modeling and Deep Learning Method

Department of Computer Science and Information Engineering

Department of Electrical and Computer Engineering

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In recent years, many studies have attempted to use deep learning for moving object detection. Some research also combines object detection methods with...

Moving Object Detection for Complex Scenes by Merging BG Modeling and Deep Learning Method

Published Online: Jun 23, 2023

Page range: 151 - 163

Received: Dec 01, 2022

Accepted: May 07, 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0012

Keywords
Video surveillance, deep learning, Moving object detection

© 2023 Chih-Yang Lin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Moving Object Detection for Complex Scenes by Merging BG Modeling and Deep Learning Method

Published Online: Jun 23, 2023

Page range: 151 - 163

Received: Dec 01, 2022

Accepted: May 07, 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0012

KeywordsVideo surveillance, deep learning, Moving object detection

© 2023 Chih-Yang Lin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Video surveillance, deep learning, Moving object detection