Joint detection and tracking of size-varying infrared targets based on block-wise sparse decomposition

Li, Miao; Lin, Zaiping; Long, Yunli; An, Wei; Zhou, Yiyu

doi:10.1016/j.infrared.2016.01.021

Cited by 5 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the low-rank property of the infrared background, some decomposition-based methods have been proposed for TIR object tracking. A block-wise sparse representation-based tracker is proposed in [29], in which the infrared image is divided into overlapped blocks. These blocks are further decomposed into low-rank target components and sparse occlusion components with adaptive weighting parameters of different parts.…”

Section: Sparse Representation-based Tir Tracking Methodsmentioning

confidence: 99%

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

Peng

Chen

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

Section: Sparse Representation-based Tir Tracking Methodsmentioning

confidence: 99%