Duality-Gated Mutual Condition Network for RGBT Tracking
文献类型：期刊论文
| 作者 | Lu, Andong1,3; Qian, Cun1,3; Li, Chenglong1,3; Tang, Jin1,3; Wang, Liang2
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2022-03-18 |
| 页码 | 14 |
| ISSN号 | 2162-237X |
| 关键词 | Target tracking Cameras Learning systems Sampling methods Noise measurement Computational modeling Computational efficiency Bidirectional feature modulation conditional learning gated scheme RGB-Thermal (RGBT) tracking |
| DOI | 10.1109/TNNLS.2022.3157594 |
| 通讯作者 | Li, Chenglong(lcl1314@foxmail.com) |
| 英文摘要 | Low-quality modalities contain not only a lot of noisy information but also some discriminative features in RGB-Thermal (RGBT) tracking. However, the potentials of low-quality modalities are not well explored in existing RGBT tracking algorithms. In this work, we propose a novel duality-gated mutual condition network to fully exploit the discriminative information of all modalities while suppressing the effects of data noise. In specific, we design a mutual condition module, which takes the discriminative information of a modality as the condition to guide feature learning of target appearance in another modality. Such a module can effectively enhance target representations of all modalities even in the presence of low-quality modalities. To improve the quality of conditions and further reduce data noise, we propose a duality-gated mechanism and integrate it into the mutual condition module. To deal with the tracking failure caused by sudden camera motion, which often occurs in RGBT tracking, we design a resampling strategy based on optical flow. It does not increase much computational cost since we perform optical flow calculation only when the model prediction is unreliable and then execute resampling when the sudden camera motion is detected. Extensive experiments on four RGBT tracking benchmark datasets show that our method performs favorably against the state-of-the-art tracking algorithms. |
| 资助项目 | University Synergy Innovation Program of Anhui Province[GXXT-2019-025] ; University Synergy Innovation Program of Anhui Province[GXXT-2021-038] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000773239300001 |
| 资助机构 | University Synergy Innovation Program of Anhui Province ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48186]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Li, Chenglong |
| 作者单位 | 1.Anhui Univ, Sch Comp Sci & Technol, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230601, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Anhui Univ, Sch Comp Sci & Technol, Anhui Prov Key Lab Multimodal Cognit Computat, Hefei 230601, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Andong,Qian, Cun,Li, Chenglong,et al. Duality-Gated Mutual Condition Network for RGBT Tracking[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:14. |
| APA | Lu, Andong,Qian, Cun,Li, Chenglong,Tang, Jin,&Wang, Liang.(2022).Duality-Gated Mutual Condition Network for RGBT Tracking.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,14. |
| MLA | Lu, Andong,et al."Duality-Gated Mutual Condition Network for RGBT Tracking".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):14. |
入库方式： OAI收割
来源：自动化研究所
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