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

Minjie Wan,Xavier Maldague,Qian Chen,Kan Ren,Hai Zhang,Weixian Qian,Guohua Gu

doi:10.3390/rs10040510

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.

Highlights

Moving target tracking has become a key technique in many emerging research applications of Smart City, such as video surveillance, intrusion monitoring, activity control, and detection of approaching objects [1]
When considering that the ε0 of deep learning tracker (DLT) in Seq.1 increases to larger than 2, we argue that DLT is robust to illumination and size changes, it fails when facing occlusion caused by disturbance target with a similar appearance
An infrared moving target tracking algorithm using total variation regularization term based low-rank and sparse representation is presented in this paper

Summary

Introduction

Moving target tracking has become a key technique in many emerging research applications of Smart City, such as video surveillance, intrusion monitoring, activity control, and detection of approaching objects [1]. Target tracking with visible cameras has been deeply investigated and a number of effective methods were proposed [2,3,4,5]. This is obviously not suitable for the nighttime environment due to its high dependency on the illumination condition. Based on the afore-mentioned discussion, we consider that it is of great necessity and significance for us to further investigate a robust infrared moving target tracking algorithm under various backgrounds

Methods

Results

Conclusion