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Total Variation Regularization Term-Based Low-Rank and Sparse Matrix Representation Model for Infrared Moving Target Tracking

1
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2
Department of Electrical and Computer Engineering, Computer Vision and Systems Laboratory, Laval University, 1065 av. de la Médecine, Quebec City, QC G1V 0A6, Canada
3
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(4), 510; https://doi.org/10.3390/rs10040510
Received: 13 January 2018 / Revised: 27 February 2018 / Accepted: 22 March 2018 / Published: 24 March 2018
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Abstract

Infrared moving target tracking plays a fundamental role in many burgeoning research areas of Smart City. Challenges in developing a suitable tracker for infrared images are particularly caused by pose variation, occlusion, and noise. In order to overcome these adverse interferences, a total variation regularization term-based low-rank and sparse matrix representation (TV-LRSMR) model is designed in order to exploit a robust infrared moving target tracker in this paper. First of all, the observation matrix that is derived from the infrared sequence is decomposed into a low-rank target matrix and a sparse occlusion matrix. For the purpose of preventing the noise pixel from being separated into the occlusion term, a total variation regularization term is proposed to further constrain the occlusion matrix. Then an alternating algorithm combing principal component analysis and accelerated proximal gradient methods is employed to separately optimize the two matrices. For long-term tracking, the presented algorithm is implemented using a Bayesien state inference under the particle filtering framework along with a dynamic model update mechanism. Both qualitative and quantitative experiments that were examined on real infrared video sequences verify that our algorithm outperforms other state-of-the-art methods in terms of precision rate and success rate. View Full-Text
Keywords: infrared moving target tracking; low-rank and sparse matrix representation; total variation regularization; particle filtering framework; Smart City infrared moving target tracking; low-rank and sparse matrix representation; total variation regularization; particle filtering framework; Smart City
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wan, M.; Gu, G.; Qian, W.; Ren, K.; Chen, Q.; Zhang, H.; Maldague, X. Total Variation Regularization Term-Based Low-Rank and Sparse Matrix Representation Model for Infrared Moving Target Tracking. Remote Sens. 2018, 10, 510.

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