Low-Rank Sparse joint Representation for Moving Object Detection in Video

Authors

Yang Lei, 1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China; ;
Pang Fang, 1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China; ;
Huosheng Hu, 2. School of Computer Science and Electrical Engineering, University of Essex, Colchester, CO4 3SQ, United Kingdom;

Abstract

Abstract: For the video sequences with fixed cameras, it is a reasonable assumption that the fixed background has low-rank characteristic, and the dynamic foreground has sparse characteristic. A new motion detection method based on low-rank and sparse joint representation is proposed in this paper. The ideas of the proposed method are described as follows: The noise of video sequence is removed by image preprocessing. The optical flow between continuous video sequences is estimated, which is used to generate a binary motion mask as a movement weight matrix. An optimization model with low-rank background and sparse foreground is established based on the idea of subspace learning theory. The background and foreground of each frame are obtained by using the ADMM-BCD iterative algorithm. Experimental results show that the proposed method is super to the other same sort of moving detection methods. The proposed method has perfect effect on slow moving target detection.

Recommended Citation

Lei, Yang; Fang, Pang; and Hu, Huosheng (2019) "Low-Rank Sparse joint Representation for Moving Object Detection in Video," Journal of System Simulation: Vol. 30: Iss. 12, Article 25.
DOI: 10.16182/j.issn1004731x.joss.201812025
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss12/25

First Page

4693

Revised Date

2018-07-02

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201812025

Last Page

4702

CLC

TP301

Recommended Citation

Yang Lei, Pang Fang, Hu Huosheng. Low-Rank Sparse joint Representation for Moving Object Detection in Video[J]. Journal of System Simulation, 2018, 30(12): 4693-4702.

DOI

10.16182/j.issn1004731x.joss.201812025