RGBT Tracking by Trident Fusion Network

doi:10.1109/TCSVT.2021.3067997

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

	RGBT Tracking by Trident Fusion Network
	Zhu, Yabin 1,3; Li, Chenglong 1,3; Tang, Jin 1,3; Luo, Bin 1,3; Wang, Liang 2
刊名	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
	2022-02-01
卷号	32 期号:2 页码:579-592
关键词	Feature extraction Convolution Target tracking Training Aggregates Visualization Benchmark testing RGBT tracking feature aggregation feature pruning trident architecture
ISSN号	1051-8215
DOI	10.1109/TCSVT.2021.3067997
通讯作者	Li, Chenglong(lcl1314@foxmail.com)
英文摘要	In recent years, RGBT tracking has become a hot topic in the field of visual tracking, and made great progress. In this paper, we propose a novel Trident Fusion Network (TFNet) to achieve effective fusion of different modalities for robust RGBT tracking. In specific, to deploy the complementarity of features of all convolutional layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in two modalities. Moreover, we design a trident architecture to integrate the fused features and both modality-specific features for robust target representations. There are three main advantages. First, retaining the classification layer of each modality is beneficial to enhance feature learning of single modality, and compared with aggregate branches, single-modality branches pay more attention to the mining of modal specific information. Second, when some modality is noisy or invalid, the modality-specific branches would capture more discriminative features for RGBT tracking. Finally, the integration of aggregation branches and single-modality branches is beneficial to the complementary learning of different modalities. In addition, we also introduce a feature pruning module in each branch to prune the redundant features and avoid network overfitting. Experimental results on four RGBT tracking benchmark datasets suggest that our tracker achieves superior performance against the state-of-the-art RGBT tracking methods.
资助项目	Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China[61976003] ; National Natural Science Foundation of China[62076003] ; National Natural Science Foundation of China[61860206004] ; Natural Science Foundation for the Higher Education Institutions of Anhui Province[KJ2020A0061] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)
WOS关键词	VISUAL TRACKING ; ROBUST TRACKING ; FILTER
WOS研究方向	Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000752017700013
资助机构	Major Project for New Generation of AI ; National Natural Science Foundation of China ; Natural Science Foundation for the Higher Education Institutions 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/173211/47602]
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, Key Lab Intelligent Comp & Signal Proc, Minist Educ, 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	Zhu, Yabin,Li, Chenglong,Tang, Jin,et al. RGBT Tracking by Trident Fusion Network[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(2):579-592.
APA	Zhu, Yabin,Li, Chenglong,Tang, Jin,Luo, Bin,&Wang, Liang.(2022).RGBT Tracking by Trident Fusion Network.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(2),579-592.
MLA	Zhu, Yabin,et al."RGBT Tracking by Trident Fusion Network".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.2(2022):579-592.