Deep Learning based Moving Object Detection for Video Surveillance

Han Yi Huang; Chih Yang Lin; Wei Yang Lin; Chien Cheng Lee; Chuan Yu Chang

doi:10.1109/ICCE-TW46550.2019.8991778

Deep Learning based Moving Object Detection for Video Surveillance

Han Yi Huang, Chih Yang Lin, Wei Yang Lin, Chien Cheng Lee, Chuan Yu Chang

Department of Mechanical Engineering

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

5 Scopus citations

Abstract

This paper proposes a new two-stream neural network which combines the traditional background modeling method with a deep learning network to detect moving objects. The input for the deep neural network is the original image and its corresponding foreground image, while the output is the bounding boxes of the moving objects in the image. Traditional CNN methods cannot distinguish moving objects from static objects, but the method in this paper successfully solves this problem.

Original language	English
Title of host publication	2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728132792
DOIs	https://doi.org/10.1109/ICCE-TW46550.2019.8991778
State	Published - May 2019
Event	6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019 - Yilan, Taiwan Duration: 20 May 2019 → 22 May 2019

Publication series

Name	2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019

Conference

Conference	6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
Country/Territory	Taiwan
City	Yilan
Period	20/05/19 → 22/05/19

Access to Document

10.1109/ICCE-TW46550.2019.8991778

Cite this

Huang, H. Y., Lin, C. Y., Lin, W. Y., Lee, C. C., & Chang, C. Y. (2019). Deep Learning based Moving Object Detection for Video Surveillance. In 2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019 Article 8991778 (2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-TW46550.2019.8991778

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title = "Deep Learning based Moving Object Detection for Video Surveillance",

abstract = "This paper proposes a new two-stream neural network which combines the traditional background modeling method with a deep learning network to detect moving objects. The input for the deep neural network is the original image and its corresponding foreground image, while the output is the bounding boxes of the moving objects in the image. Traditional CNN methods cannot distinguish moving objects from static objects, but the method in this paper successfully solves this problem.",

author = "Huang, {Han Yi} and Lin, {Chih Yang} and Lin, {Wei Yang} and Lee, {Chien Cheng} and Chang, {Chuan Yu}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019 ; Conference date: 20-05-2019 Through 22-05-2019",

year = "2019",

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Huang, HY, Lin, CY, Lin, WY, Lee, CC & Chang, CY 2019, Deep Learning based Moving Object Detection for Video Surveillance. in 2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019., 8991778, 2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019, Institute of Electrical and Electronics Engineers Inc., 6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019, Yilan, Taiwan, 20/05/19. https://doi.org/10.1109/ICCE-TW46550.2019.8991778

Deep Learning based Moving Object Detection for Video Surveillance. / Huang, Han Yi; Lin, Chih Yang; Lin, Wei Yang et al.
2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8991778 (2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019).

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

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AU - Lin, Chih Yang

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AU - Lee, Chien Cheng

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PY - 2019/5

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N2 - This paper proposes a new two-stream neural network which combines the traditional background modeling method with a deep learning network to detect moving objects. The input for the deep neural network is the original image and its corresponding foreground image, while the output is the bounding boxes of the moving objects in the image. Traditional CNN methods cannot distinguish moving objects from static objects, but the method in this paper successfully solves this problem.

AB - This paper proposes a new two-stream neural network which combines the traditional background modeling method with a deep learning network to detect moving objects. The input for the deep neural network is the original image and its corresponding foreground image, while the output is the bounding boxes of the moving objects in the image. Traditional CNN methods cannot distinguish moving objects from static objects, but the method in this paper successfully solves this problem.

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T3 - 2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019

BT - 2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019

Y2 - 20 May 2019 through 22 May 2019

ER -

Huang HY, Lin CY, Lin WY, Lee CC, Chang CY. Deep Learning based Moving Object Detection for Video Surveillance. In 2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8991778. (2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019). doi: 10.1109/ICCE-TW46550.2019.8991778

Deep Learning based Moving Object Detection for Video Surveillance

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