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

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.

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

“…Scholars have proposed many methods for cloud detection of temporal infrared data using observation images of more than two scenes at the same location but at different times [10]- [13]. Many artificial intelligence methods have also been used for satellite infrared image target detection [14]- [19], but these methods are not suitable for cirrus detection in real scenes because they require a large quantity of image data [20].…”

“…to represent the objective function in equation (20), and use optimization condition This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.…”

Infrared Image Super-Resolution Reconstruction Based on Quaternion and High-Order Overlapping Group Sparse Total Variation

“…Image super-resolution reconstruction (SRR) uses digital signal processing to generate high-resolution (HR) images from a single or multiple frames of low-resolution (LR) images, mainly through the super-resolution method. Image super-resolution reconstruction can efficiently utilize the potential value of existing image data and has applications such as military remote sensing reconnaissance [1], target tracking and monitoring [2,3,4], target location and recognition [5], astronomical observation [6], and medical imaging [7].…”

Editorial for Special Issue “Remote Sensing for Target Object Detection and Identification”

“…The third group of papers [10][11][12][13][14] focuses on object detection using infrared sensors. Zhang et al [10] propose a method based on a low rank sparse decomposition that uses a non-convex optimization with an Lp-norm constraint in order to identify small targets in sequences of infrared images.…”

“…The final target identification paper is focused on a flux density-based algorithm, which is able to identify the different infrared gradient vector fields between target and noise. Li et al [13] present a thermal infrared (TIR) target tracking algorithm based on semantic features. Specifically, a mask sparse representation is used to distinguish the reliable pixels (for target tracking) from the unreliable ones in each TIR frame.…”