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Signal, Image and Video Processing

, Volume 13, Issue 8, pp 1593–1601 | Cite as

Detecting moving objects via the low-rank representation

  • Yang Zhou
  • Bingo Wing-Kuen LingEmail author
Original Paper
First Online:
  • 75 Downloads

Abstract

Moving object detection is a fundamental and necessary step in many computer vision algorithms. These algorithms are built in many intelligent devices such as in the smartphones, the tachographs and the personal video recorders. Recently, the methods for performing the moving object detection based on the low-rank representation have been proposed. For these methods, it is assumed that the background is represented by a low-rank matrix. On the other hand, the foreground objects cannot be represented by low-rank matrices. They are seen as the outliers. Hence, detecting the contiguous outliers in the low-rank representation (DECELOR) can be formulated as an extension of the robust principal component analysis problem. This method fully utilizes the spatial continuity of the foreground regions. To achieve a more accurate detection, this paper integrates both the concave penalty function and the priori target rank information into a single optimization problem based on the DECELOR formulation. The optimization problem is efficiently solved by an alternating direction scheme. The computer numerical simulation results on the real-world scenes demonstrate the superiority of our method in terms of the effective handling of a wide range of complex scenarios.

Keywords

Moving object detection Low-rank representation Concave penalty function Priori target rank information 
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Notes

Acknowledgements

This paper is supported partly by the National Nature Science Foundation of China (Nos. U1701266, 61372173, 61471132 and 61671163), the Guangdong Higher Education Engineering Technology Research Center for Big Data on Manufacturing Knowledge Patent (No. 501130144), the Natural Science Foundation of Guangdong Province China (No. 2014A030310346) and the Science and Technology Planning Project of Guangdong Province China (No. 2015A030401090).

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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Information EngineeringGuangdong University of TechnologyGuangzhouChina

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