Next Article in Journal
Time-Universal Data Compression
Previous Article in Journal
Poisson Twister Generator by Cumulative Frequency Technology
Open AccessArticle

Combining Background Subtraction and Convolutional Neural Network for Anomaly Detection in Pumping-Unit Surveillance

School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
Author to whom correspondence should be addressed.
Algorithms 2019, 12(6), 115;
Received: 17 April 2019 / Revised: 23 May 2019 / Accepted: 24 May 2019 / Published: 29 May 2019
PDF [6520 KB, uploaded 29 May 2019]


Background subtraction plays a fundamental role for anomaly detection in video surveillance, which is able to tell where moving objects are in the video scene. Regrettably, the regular rotating pumping unit is treated as an abnormal object by the background-subtraction method in pumping-unit surveillance. As an excellent classifier, a deep convolutional neural network is able to tell what those objects are. Therefore, we combined background subtraction and a convolutional neural network to perform anomaly detection for pumping-unit surveillance. In the proposed method, background subtraction was applied to first extract moving objects. Then, a clustering method was adopted for extracting different object types that had more movement-foreground objects but fewer typical targets. Finally, nonpumping unit objects were identified as abnormal objects by the trained classification network. The experimental results demonstrate that the proposed method can detect abnormal objects in a pumping-unit scene with high accuracy. View Full-Text
Keywords: background subtraction; transfer learning; classification background subtraction; transfer learning; classification

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).

Share & Cite This Article

MDPI and ACS Style

Yu, T.; Yang, J.; Lu, W. Combining Background Subtraction and Convolutional Neural Network for Anomaly Detection in Pumping-Unit Surveillance. Algorithms 2019, 12, 115.

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



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top