Limited-Anchor Deep Neural Network for Moving Object Detection

Chih Yang Lin; Han Yi Huang; Wei Yang Lin; Chuan Yu Chang; Wen Thong Chang; Yih Kuen Jan

doi:10.1109/ICCE-Taiwan49838.2020.9258320

Limited-Anchor Deep Neural Network for Moving Object Detection

Chih Yang Lin, Han Yi Huang, Wei Yang Lin, Chuan Yu Chang, Wen Thong Chang, Yih Kuen Jan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper proposes a new method that integrates a deep learning based object detection network into traditional background modeling to detect moving objects. The proposed method allows us to efficiently identify candidates that contain moving objects while only setting a small number of anchors in the moving area of the image through guidance from the traditional background modeling method. This paper overcomes the disadvantages of conventional background modeling methods and conventional deep learning based object detection methods in terms of dynamic backgrounds and objects' motion states.

Original language	English (US)
Title of host publication	2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728173993
DOIs	https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258320
State	Published - Sep 28 2020
Externally published	Yes
Event	7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan, Province of China Duration: Sep 28 2020 → Sep 30 2020

Publication series

Name	2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference	7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Country/Territory	Taiwan, Province of China
City	Taoyuan
Period	9/28/20 → 9/30/20

ASJC Scopus subject areas

Computer Networks and Communications
Artificial Intelligence
Computer Science Applications
Signal Processing
Electrical and Electronic Engineering
Instrumentation

Online availability

10.1109/ICCE-Taiwan49838.2020.9258320

Library availability

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Cite this

Lin, C. Y., Huang, H. Y., Lin, W. Y., Chang, C. Y., Chang, W. T., & Jan, Y. K. (2020). Limited-Anchor Deep Neural Network for Moving Object Detection. In 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 Article 9258320 (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258320

Limited-Anchor Deep Neural Network for Moving Object Detection. / Lin, Chih Yang; Huang, Han Yi; Lin, Wei Yang et al.
2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9258320 (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lin, CY, Huang, HY, Lin, WY, Chang, CY, Chang, WT & Jan, YK 2020, Limited-Anchor Deep Neural Network for Moving Object Detection. in 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020., 9258320, 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020, Institute of Electrical and Electronics Engineers Inc., 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020, Taoyuan, Taiwan, Province of China, 9/28/20. https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258320

Lin CY, Huang HY, Lin WY, Chang CY, Chang WT, Jan YK. Limited-Anchor Deep Neural Network for Moving Object Detection. In 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9258320. (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020). doi: 10.1109/ICCE-Taiwan49838.2020.9258320

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title = "Limited-Anchor Deep Neural Network for Moving Object Detection",

abstract = "This paper proposes a new method that integrates a deep learning based object detection network into traditional background modeling to detect moving objects. The proposed method allows us to efficiently identify candidates that contain moving objects while only setting a small number of anchors in the moving area of the image through guidance from the traditional background modeling method. This paper overcomes the disadvantages of conventional background modeling methods and conventional deep learning based object detection methods in terms of dynamic backgrounds and objects' motion states.",

author = "Lin, {Chih Yang} and Huang, {Han Yi} and Lin, {Wei Yang} and Chang, {Chuan Yu} and Chang, {Wen Thong} and Jan, {Yih Kuen}",

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AB - This paper proposes a new method that integrates a deep learning based object detection network into traditional background modeling to detect moving objects. The proposed method allows us to efficiently identify candidates that contain moving objects while only setting a small number of anchors in the moving area of the image through guidance from the traditional background modeling method. This paper overcomes the disadvantages of conventional background modeling methods and conventional deep learning based object detection methods in terms of dynamic backgrounds and objects' motion states.

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M3 - Conference contribution

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BT - 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

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Limited-Anchor Deep Neural Network for Moving Object Detection

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