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Refinement of Background-Subtraction Methods Based on Convolutional Neural Network Features for Dynamic Background

School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
Author to whom correspondence should be addressed.
Algorithms 2019, 12(7), 128;
Received: 28 April 2019 / Revised: 20 June 2019 / Accepted: 25 June 2019 / Published: 27 June 2019
(This article belongs to the Special Issue Deep Learning for Image and Video Understanding)
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Advancing the background-subtraction method in dynamic scenes is an ongoing timely goal for many researchers. Recently, background subtraction methods have been developed with deep convolutional features, which have improved their performance. However, most of these deep methods are supervised, only available for a certain scene, and have high computational cost. In contrast, the traditional background subtraction methods have low computational costs and can be applied to general scenes. Therefore, in this paper, we propose an unsupervised and concise method based on the features learned from a deep convolutional neural network to refine the traditional background subtraction methods. For the proposed method, the low-level features of an input image are extracted from the lower layer of a pretrained convolutional neural network, and the main features are retained to further establish the dynamic background model. The evaluation of the experiments on dynamic scenes demonstrates that the proposed method significantly improves the performance of traditional background subtraction methods. View Full-Text
Keywords: background-subtraction; convolutional neural network; convolutional features background-subtraction; convolutional neural network; convolutional features

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Yu, T.; Yang, J.; Lu, W. Refinement of Background-Subtraction Methods Based on Convolutional Neural Network Features for Dynamic Background. Algorithms 2019, 12, 128.

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