CASIA OpenIR
Cross-modality interactive attention network for multispectral pedestrian detection
Zhang, Lu1,4; Liu, Zhiyong1,4,7; Zhang, Shifeng2,3,4; Yang, Xu1,4; Qiao, Hong1,4,7; Huang, Kaizhu5; Hussain, Amir6
Source PublicationINFORMATION FUSION
ISSN1566-2535
2019-10-01
Volume50Pages:20-29
Corresponding AuthorLiu, Zhiyong(zhiyong.liu@ia.ac.cn)
AbstractMultispectral 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.
KeywordPedestrian detection Modality fusion Cross-modality attention Deep neural networks
DOI10.1016/j.inffus.2018.09.015
WOS KeywordFUSION ; IMAGES
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS IDWOS:000466056900003
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24395
Collection中国科学院自动化研究所
Corresponding AuthorLiu, Zhiyong
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
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.
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