Moving Object Detection Based on Deep Convolutional Neural Network

Authors

Yuqiu Lu, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;
Jinyu Sun, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;
Shiwei Ma, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;

Abstract

Abstract: A deep convolution neural network MONet is designed for the intermittent motion problem in moving objects detection. In the absence of training data sets, a synthetic dataset GoChairs is generated by affine transformation, and on this basis the network training and testing are performed. The results show that the trained MONet can effectively detect the moving objects based on the correspondence between the pixels. The traditional datasets CDnet and I2R are also tested to verify the generalization performance of the network. In addition, MONet is compared qualitatively and quantitatively with classical methods for the intermittent motion problem of the objects. The experimental results demonstrate the superiority of the network in detecting the objects with intermittent motion.

Recommended Citation

Lu, Yuqiu; Sun, Jinyu; and Ma, Shiwei (2019) "Moving Object Detection Based on Deep Convolutional Neural Network," Journal of System Simulation: Vol. 31: Iss. 11, Article 10.
DOI: 10.16182/j.issn1004731x.joss.19-FZ0368
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss11/10

First Page

2275

Revised Date

2019-07-23

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.19-FZ0368

Last Page

2280

CLC

TP391.4

Recommended Citation

Lu Yuqiu, Sun Jinyu, Ma Shiwei. Moving Object Detection Based on Deep Convolutional Neural Network[J]. Journal of System Simulation, 2019, 31(11): 2275-2280.

Corresponding Author

Shiwei Ma,

DOI

10.16182/j.issn1004731x.joss.19-FZ0368