Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking
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 th...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-08-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/11/17/1967 |
| _version_ | 1818322565952700416 |
|---|---|
| author | Meihui Li Lingbing Peng Yingpin Chen Suqi Huang Feiyi Qin Zhenming Peng |
| author_facet | Meihui Li Lingbing Peng Yingpin Chen Suqi Huang Feiyi Qin Zhenming Peng |
| author_sort | Meihui Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T10:58:50Z |
| format | Article |
| id | doaj.art-b4dee3f8b8b1436da44e805877ddc0b3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:58:50Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b4dee3f8b8b1436da44e805877ddc0b32022-12-21T23:49:21ZengMDPI AGRemote Sensing2072-42922019-08-011117196710.3390/rs11171967rs11171967Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target TrackingMeihui Li0Lingbing Peng1Yingpin Chen2Suqi Huang3Feiyi Qin4Zhenming Peng5School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Physics and Information, Minnan Normal University, Zhangzhou 363000, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThermal 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.https://www.mdpi.com/2072-4292/11/17/1967thermal infrared target trackingsemantic featuresmask sparse representationparticle filter frameworkADMM |
| spellingShingle | Meihui Li Lingbing Peng Yingpin Chen Suqi Huang Feiyi Qin Zhenming Peng Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking Remote Sensing thermal infrared target tracking semantic features mask sparse representation particle filter framework ADMM |
| title | Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking |
| title_full | Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking |
| title_fullStr | Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking |
| title_full_unstemmed | Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking |
| title_short | Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking |
| title_sort | mask sparse representation based on semantic features for thermal infrared target tracking |
| topic | thermal infrared target tracking semantic features mask sparse representation particle filter framework ADMM |
| url | https://www.mdpi.com/2072-4292/11/17/1967 |
| work_keys_str_mv | AT meihuili masksparserepresentationbasedonsemanticfeaturesforthermalinfraredtargettracking AT lingbingpeng masksparserepresentationbasedonsemanticfeaturesforthermalinfraredtargettracking AT yingpinchen masksparserepresentationbasedonsemanticfeaturesforthermalinfraredtargettracking AT suqihuang masksparserepresentationbasedonsemanticfeaturesforthermalinfraredtargettracking AT feiyiqin masksparserepresentationbasedonsemanticfeaturesforthermalinfraredtargettracking AT zhenmingpeng masksparserepresentationbasedonsemanticfeaturesforthermalinfraredtargettracking |