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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
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Algorithms 2019, 12(6), 115; https://doi.org/10.3390/a12060115
Received: 17 April 2019 / Revised: 23 May 2019 / Accepted: 24 May 2019 / Published: 29 May 2019
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Abstract

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

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