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Cross-modality interactive attention network for multispectral pedestrian detection

文献类型：期刊论文

作者Zhang, Lu1,4; Liu, Zhiyong1,4,7; Zhang, Shifeng2,3,4; Yang, Xu1,4; Qiao, Hong1,4,7; Huang, Kaizhu5; Hussain, Amir6
刊名INFORMATION FUSION
出版日期2019-10-01
卷号50页码:20-29
关键词Pedestrian detection Modality fusion Cross-modality attention Deep neural networks
ISSN号1566-2535
DOI10.1016/j.inffus.2018.09.015
通讯作者Liu, Zhiyong(zhiyong.liu@ia.ac.cn)
英文摘要Multispectral pedestrian detection is an emerging solution with great promise in many around-the-clock applications, such as automotive driving and security surveillance. To exploit the complementary nature and remedy contradictory appearance between modalities, in this paper, we propose a novel cross-modality interactive attention network that takes full advantage of the interactive properties of multispectral input sources. Specifically, we first utilize the color (RGB) and thermal streams to build up two detached feature hierarchy for each modality, then by taking the global features, correlations between two modalities are encoded in the attention module. Next, the channel responses of halfway feature maps are recalibrated adaptively for subsequent fusion operation. Our architecture is constructed in the multi-scale format to better deal with different scales of pedestrians, and the whole network is trained in an end-to-end way. The proposed method is extensively evaluated on the challenging KAIST multispectral pedestrian dataset and achieves state-of-the-art performance with high efficiency.
WOS关键词FUSION ; IMAGES
资助项目National Key Research and Development Plan of China[2017YFB1300202] ; National Key Research and Development Plan of China[2016YFC0300801] ; National Natural Science Foundation of China[U1613213] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[61503383] ; National Natural Science Foundation of China[61210009] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61702516] ; National Natural Science Foundation of China[61473236] ; National Natural Science Foundation of China[61876155] ; Ministry of Science and Technology of the People's Republic of China[2015BAK35B00] ; Ministry of Science and Technology of the People's Republic of China[2015BAK35B01] ; Chinese Academy of Sciences (Science Frontier Program)[XDBS01050100] ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China[17KJD520010] ; Guangdong Science and Technology Department[2016B090910001] ; Suzhou Science and Technology Program[SYG201712] ; Suzhou Science and Technology Program[SZS201613] ; Key Program Special Fund in XJTLU[KSF-A-01] ; Key Program Special Fund in XJTLU[KSF-P-02] ; UK Engineering and Physical Sciences Research Council (EPSRC)[EP/M026981/1]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000466056900003
出版者ELSEVIER SCIENCE BV
资助机构National Key Research and Development Plan of China ; National Natural Science Foundation of China ; Ministry of Science and Technology of the People's Republic of China ; Chinese Academy of Sciences (Science Frontier Program) ; Natural Science Foundation of the Jiangsu Higher Education Institutions of China ; Guangdong Science and Technology Department ; Suzhou Science and Technology Program ; Key Program Special Fund in XJTLU ; UK Engineering and Physical Sciences Research Council (EPSRC)
源URL[http://ir.ia.ac.cn/handle/173211/24394]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者Liu, Zhiyong
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Ctr Biometr & Secur Res, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
5.Xian Jiaotong Liverpool Univ, Dept EEE, SIP, Renai Rd 111, Suzhou 215123, Jiangsu, Peoples R China
6.Edinburgh Napier Univ, Sch Comp, Merchiston Campus, Edinburgh EH10 5DT, Midlothian, Scotland
7.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Lu,Liu, Zhiyong,Zhang, Shifeng,et al. Cross-modality interactive attention network for multispectral pedestrian detection[J]. INFORMATION FUSION,2019,50:20-29.
APA Zhang, Lu.,Liu, Zhiyong.,Zhang, Shifeng.,Yang, Xu.,Qiao, Hong.,...&Hussain, Amir.(2019).Cross-modality interactive attention network for multispectral pedestrian detection.INFORMATION FUSION,50,20-29.
MLA Zhang, Lu,et al."Cross-modality interactive attention network for multispectral pedestrian detection".INFORMATION FUSION 50(2019):20-29.

入库方式： OAI收割

来源：自动化研究所

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