Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking

Meihui Li,Feiyi Qin,Zhenming Peng,Yingpin Chen,Suqi Huang,Lingbing Peng

doi:10.3390/rs11171967

Abstract

Thermal infrared (TIR) target tracking is a challenging task as it entails learning an effective model to identify the target in the situation of poor target visibility and clutter background. The sparse representation, as a typical appearance modeling approach, has been successfully exploited in the TIR target tracking. However, the discriminative information of the target and its surrounding background is usually neglected in the sparse coding process. To address this issue, we propose a mask sparse representation (MaskSR) model, which combines sparse coding together with high-level semantic features for TIR target tracking. We first obtain the pixel-wise labeling results of the target and its surrounding background in the last frame, and then use such results to train target-specific deep networks using a supervised manner. According to the output features of the deep networks, the high-level pixel-wise discriminative map of the target area is obtained. We introduce the binarized discriminative map as a mask template to the sparse representation and develop a novel algorithm to collaboratively represent the reliable target part and unreliable target part partitioned with the mask template, which explicitly indicates different discriminant capabilities by label 1 and 0. The proposed MaskSR model controls the superiority of the reliable target part in the reconstruction process via a weighted scheme. We solve this multi-parameter constrained problem by a customized alternating direction method of multipliers (ADMM) method. This model is applied to achieve TIR target tracking in the particle filter framework. To improve the sampling effectiveness and decrease the computation cost at the same time, a discriminative particle selection strategy based on kernelized correlation filter is proposed to replace the previous random sampling for searching useful candidates. Our proposed tracking method was tested on the VOT-TIR2016 benchmark. The experiment results show that the proposed method has a significant superiority compared with various state-of-the-art methods in TIR target tracking.

Highlights

With the improvement of the imaging quality and resolution of thermal cameras, thermal infrared (TIR) target tracking has begun to attract many researchers’ attention in recent years
Considering the benefit of strong discriminative ability of the deep convolutional neural networks (DCNN) [7,8,10,11,24], we propose a supervised learning manner to extract high-level semantic features of the target area
To improve the ability of distinguishing the target from the clutter background, we propose a mask sparse representation method for target appearance modeling

Summary

Introduction

With the improvement of the imaging quality and resolution of thermal cameras, thermal infrared (TIR) target tracking has begun to attract many researchers’ attention in recent years. Compared with visual target tracking, TIR target tracking is capable of working in total darkness and is less susceptible to changes in external environment, such as lighting and shadows. It is important for both military and civil use [1,2]. The TIR images have the characteristics of low-contrast, low signal-to-noise ratio, low signal-to-clutter ratio and lack of color information [3,4], which cause a lot of difficulty in distinguishing the moving target from the background. The deformation and scale change of the moving target bring great challenges to the tracking task

Objectives

Methods

Results

Conclusion