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

doi:10.16182/j.issn1004731x.joss.201812025

Journal of System Simulation ›› 2018, Vol. 30 ›› Issue (12): 4693-4702.doi: 10.16182/j.issn1004731x.joss.201812025

Previous Articles Next Articles

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

Yang Lei¹, Pang Fang¹, Hu Huosheng²

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

Received:2018-04-20 Revised:2018-07-02 Online:2018-12-10 Published:2019-01-03
About author:Yang Lei (1976-), male, Shanxi, China, an associate researcher and postgraduate advisor; research interests: artificial intelligence and pattern recognition, computer vision and image processing.
Supported by:
National Natural Science Foundation of China (31100709), Shanghai science and Technology Committee (18411952200)

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.

Key words: robust principal component analysis (RPCA), subspace learning, background-foreground modeling, motion detection

CLC Number:

TP301

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.

References

[1] T B Moeslund, A Hilton, V Kruger.A survey of advances in vision-based human motion capture and analysis[J]. Computer Vision & Image Understanding (S1077-3142), 2006, 104(2): 90-126.
[2] R Poppe.A survey on vision-based human action recognition[J]. Image & Vision Computing (S0262-8856), 2010, 28(6): 976-990.
[3] A Ilyas, M Scuturici, S Miguet.Real-time foreground-background segmentation using a modified codebook model[C]. IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009, 10: 454-459.
[4] A Mittal, N Paragios.Motion-based background subtraction using adaptive kernel density estimation[C]. IEEE Computer Society Conference on Computer Vision & Pattern Recognition (S1063-6919), 2004, 2: 302-309.
[5] O Barnich, M Van Droogenbroeck.ViBe: A universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing (S1057-7149), 2011, 20(6): 1709-1724.
[6] E J Candès, X Li, Y Ma, et al.Robust principal component analysis?. [J]. ACM (S0004-5411), 2011, 58(3): 1-37.
[7] Y Peng, A Ganesh, J Wright, et al.RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2012, 34(11): 2233-2246.
[8] T Zhou, D Tao.GoDec: randomized low-rank and sparse matrix decomposition in noisy case[C]. International Conference on Machine Learning, 2011, 33-40.
[9] Q Ke, T Kanade.Robust l-Norm Factorization in the Presence of Outliers and Missing Data by Alternative Convex Programming[C]. IEEE Computer Society Conference on Computer Vision & Pattern Recognition:(S1063-6919), 2005: 739-746.
[10] Y Shen, Z Wen, Y Zhang.Augmented lagrangian alternating direction method for matrix separation based on low-rank factorization[J]. Optimization Methods & Software(S1055-6788), 2011, 29(2): 239-263.
[11] Xianbiao Shu, Fatih Porikli, Narendra Ahuja.Robust Orthonormal Subspace Learning: Efficient Recovery of Corrupted Low-Rank Matrices[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition:(S1063-6919), 2014, 1: 3874-3881.
[12] Xinchen Ye, Jingyu Yang, Xin Sun, et al.Foreground-Background Separation From Video Clips via Motion-Assisted Matrix Restoration[J]. IEEE Transactions on Circuits and Systems for Video Technology (S1051-8215), 2015, 25(11): 1721-1734.
[13] S Javed, A Mahmood, T Bouwmans, et al.Background- Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering[J]. IEEE Transactions on Image Processing (S1057-7149), 2017, 26(12): 5840-5854.
[14] N Shahid, V Kalofolias, X Bresson, et al.Robust Principal Component Analysis on Graphs[C]. 2015 IEEE International Conference on Computer Vision (ICCV) :(S1550-5499), 2015: 2812-2820.
[15] S Boyd, N Parikh, E Chu, et al.Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers[J]. Foundations and Trends in Machine Learning:(S1935-8237), 2011, 3(1): 1-122.
[16] S Bengio, F Pereira, Y Singer, et al.Group sparse coding[C]. International Conference on Neural Information Processing Systems (S1049-5258), 2009, 22(1): 82-89.
[17] C Guyon, T Bouwmans, E H Zahzah.Moving Object Detection by Robust PCA Solved via a Linearized Symmetric Alternating Direction Method[C]. International Symposium on Visual Computing:(S0302-9743), 2013, 7431: 427-436.

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

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Xiangyang Deng, Limin Zhang, Wei Fang, Miao Tang. Robot Path Planning Based on Bidirectional Aggregation Ant Colony Optimization [J]. Journal of System Simulation, 2022, 34(5): 1101-1108.
[2]	Tao Ning, Tao Gou, Xiangdong Liu. Simulation on Cold Chain Distribution Path of Fresh Agricultural Products Under Low-carbon Constraints [J]. Journal of System Simulation, 2022, 34(4): 797-805.
[3]	Jianfeng Li, Di Lu, Hexiang Li. An Improved Atomic Search Algorithm [J]. Journal of System Simulation, 2022, 34(3): 490-502.
[4]	Jun Dong, Chunming Ye. Research on Joint Optimization of Energy-Saving Distributed Manufacturing and Preventive Maintenance for Semiconductor Wafers [J]. Journal of System Simulation, 2022, 34(3): 584-602.
[5]	Sun Xiaoqing, Cheng Hao, Zhang Luyao, Ji Weidong, Wang Xu. A Natural Computing Method Based on Spatial Division Search Strategy [J]. Journal of System Simulation, 2021, 33(11): 2589-2605.
[6]	Wang Fuyu, Tang Tao. Application of Two-population Fish Swarm Algorithm in Distributed Portfolio [J]. Journal of System Simulation, 2021, 33(9): 2074-2084.
[7]	Duan Hong, Qiu Xiaogang. Networked Simulation and It's Development Trend [J]. Journal of System Simulation, 2021, 33(7): 1526-1533.
[8]	Duan Hong, Qiu Xiaogang, Xie Xu, Ju Rusheng. Study on Composition of Simulation Discipline Knowledge Areas [J]. Journal of System Simulation, 2021, 33(4): 763-772.
[9]	Liu Jingsen, Yuan Mengmeng, Li Yu. Solving Engineering Optimization Design Problems Based on Improved Salp Swarm Algorithm [J]. Journal of System Simulation, 2021, 33(4): 854-866.
[10]	Gu Juping, Cheng Tianyu, Wang Jianping, Hua Liang, Zhao Fengshen, Jiang Ling. Fast 3D Medical Image Registration Based on Geometric Feature Invariants [J]. Journal of System Simulation, 2020, 32(11): 2105-2111.
[11]	Ni Wanlu, Ji Weidong, Sun Xiaoqing. Nature Computation of Self-Adaptive Dynamic Control Strategy of Population Grouping [J]. Journal of System Simulation, 2020, 32(10): 1884-1894.
[12]	Lu Zhanwen, Cheng Xingong, Zhang Yongfeng. Global Optimization Method Based on Consensus Particle Swarm Optimization [J]. Journal of System Simulation, 2020, 32(10): 1936-1942.
[13]	Zhang Luyao, Ji Weidong, Cheng Hao. Natural Computing Method Based on LLE Dimension Reduction [J]. Journal of System Simulation, 2020, 32(10): 1943-1955.
[14]	He Yitao, Li Jun, Hao Liyan. Bacterial Foraging Algorithm with Gravitational Mechanism [J]. Journal of System Simulation, 2020, 32(9): 1724-1735.
[15]	Liu Changliang, Wang Ziqi. Structure Learning of Fuzzy-tree Based on Rigorous Binary Tree Code and Genetic Algorithm [J]. Journal of System Simulation, 2020, 32(8): 1473-1480.