Next Article in Journal / Special Issue
Deep Learning with a Spatiotemporal Descriptor of Appearance and Motion Estimation for Video Anomaly Detection
Previous Article in Journal
Optimization Based Evaluation of Grating Interferometric Phase Stepping Series and Analysis of Mechanical Setup Instabilities
Previous Article in Special Issue
Background Subtraction for Moving Object Detection in RGBD Data: A Survey
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
J. Imaging 2018, 4(6), 78; https://doi.org/10.3390/jimaging4060078

Analytics of Deep Neural Network-Based Background Subtraction

Graduate School of Information Science and Electrical Engineering, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan
*
Author to whom correspondence should be addressed.
Received: 14 May 2018 / Revised: 5 June 2018 / Accepted: 5 June 2018 / Published: 8 June 2018
(This article belongs to the Special Issue Detection of Moving Objects)
Full-Text   |   PDF [34950 KB, uploaded 8 June 2018]   |  

Abstract

Deep neural network-based (DNN-based) background subtraction has demonstrated excellent performance for moving object detection. The DNN-based background subtraction automatically learns the background features from training images and outperforms conventional background modeling based on handcraft features. However, previous works fail to detail why DNNs work well for change detection. This discussion helps to understand the potential of DNNs in background subtraction and to improve DNNs. In this paper, we observe feature maps in all layers of a DNN used in our investigation directly. The DNN provides feature maps with the same resolution as that of the input image. These feature maps help to analyze DNN behaviors because feature maps and the input image can be simultaneously compared. Furthermore, we analyzed important filters for the detection accuracy by removing specific filters from the trained DNN. From the experiments, we found that the DNN consists of subtraction operations in convolutional layers and thresholding operations in bias layers and scene-specific filters are generated to suppress false positives from dynamic backgrounds. In addition, we discuss the characteristics and issues of the DNN based on our observation. View Full-Text
Keywords: background subtraction; background modeling; convolutional neural network background subtraction; background modeling; convolutional neural network
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Minematsu, T.; Shimada, A.; Uchiyama, H.; Taniguchi, R.-I. Analytics of Deep Neural Network-Based Background Subtraction. J. Imaging 2018, 4, 78.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top