Duality-Gated Mutual Condition Network for RGBT Tracking

doi:10.1109/TNNLS.2022.3157594

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能感知与计算研究中心

	Duality-Gated Mutual Condition Network for RGBT Tracking
	Lu, Andong 1,2; Qian, Cun 1,2; Li, Chenglong 1,2; Tang, Jin 1,2; Wang, Liang 3
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2022-03-18
页码	14
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
ISSN号	2162-237X
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)
内容类型	期刊论文
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, Anhui Prov Key Lab Multimodal Cognit Computat, Hefei 230601, Peoples R China 2.Anhui Univ, Sch Comp Sci & Technol, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230601, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, 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.