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Novel event analysis for human-machine collaborative underwater exploration
Cong Y(丛杨)1,2; Fan BJ(范保杰)4; Hou DD(侯冬冬)1,2,3; Fan HJ(范慧杰)1,2; Liu KZ(刘开周)1,2; Luo JB(罗杰波)5
Department机器人学研究室
Corresponding AuthorCong, Yang(congyang81@gmail.com)
Source PublicationPattern Recognition
ISSN0031-3203
2019
Volume96Pages:1-11
Indexed BySCI ; EI
EI Accession number20193107265356
WOS IDWOS:000487569700028
Contribution Rank1
Funding OrganizationNational Nature Science Foundation under Grant (61722311, U1613214, 61821005, 61533015) ; CAS-Youth Innovation Promotion Association Scholarship (2012163) ; Liaoning Revitalization Talents Program (XLYC1807053).
KeywordUnderwater Underwater robot Visual summarization Visual saliency Visual tracking Robot vision Video analysis Novel event Deep sea
AbstractOne of the main task for deep sea submersible is for human-machine collaborative scientific exploration, e.g., human ourselves drive the submersible and monitor cameras around the submersible to observe new species fish or strange topography in a tedious way. In this paper, by defining novel marine animals or any extreme events as novel events, we design a new deep sea novel visual event analysis framework to improve the efficiency of human-machine collaboration and improve the accuracy simultaneously. Specifically, our visual framework concerns diverse functions than most state-of-the-arts, including novel event detection, tracking and summarization. Due to the power and computation resource limitation of the submersible, we design an efficient deep learning based visual saliency method for novel event detection and propose an online object tracking strategy as well. All the experiments are depending on Chinese Jiaolong, the manned deep sea submersible, which mounts several PanCtiltCzoom (PTZ) camera and static cameras. We build a new novel deep sea event dataset and the results justify that our human-machine collaborative visual observation framework can automatically detect, track and summarize the novel deep sea event.
Language英语
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS KeywordTAGGED LEOPARD SHARK ; VISUAL-ATTENTION ; LIVE FISH ; TRACKING ; MODEL
WOS Research AreaComputer Science ; Engineering
Funding ProjectNational Nature Science Foundation[61722311] ; National Nature Science Foundation[U1613214] ; National Nature Science Foundation[61821005] ; National Nature Science Foundation[61533015] ; CAS-Youth Innovation Promotion Association Scholarship[2012163] ; Liaoning Revitalization Talents Program[XLYC1807053]
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25387
Collection机器人学研究室
Corresponding AuthorCong Y(丛杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China
3.University of Chinese Academy of Sciences, China
4.College of Automation, Nanjing University of Posts and Telecommunications, China
5.Department of Computer Science, University of Rochester, United States
Recommended Citation
GB/T 7714
Cong Y,Fan BJ,Hou DD,et al. Novel event analysis for human-machine collaborative underwater exploration[J]. Pattern Recognition,2019,96:1-11.
APA Cong Y,Fan BJ,Hou DD,Fan HJ,Liu KZ,&Luo JB.(2019).Novel event analysis for human-machine collaborative underwater exploration.Pattern Recognition,96,1-11.
MLA Cong Y,et al."Novel event analysis for human-machine collaborative underwater exploration".Pattern Recognition 96(2019):1-11.
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