Probability Density Estimation for Object Recognition in Unmanned Aerial Vehicle Application

被引：0

作者：

Kharchenko, V. P. ^{[1
]}

Kukush, A. G. ^{[2
]}

Kuzmenko, N. S. ^{[3
]}

Ostroumov, I. V. ^{[3
]}

机构：

[1] Natl Aviat Univ, Kiev, Ukraine

[2] Taras Shevchenko Natl Univ Kyiv, Fac Mech & Math, Kiev, Ukraine

[3] Purdue Univ, Sch Aeronaut & Astronaut, W Lafayette, IN 47907 USA

来源：

2017 IEEE 4TH INTERNATIONAL CONFERENCE ACTUAL PROBLEMS OF UNMANNED AERIAL VEHICLES DEVELOPMENTS (APUAVD) | 2017年

关键词：

Bayesian approach; frame; Nadaraya-Watson estimate; nonparametric regression; object recognition; probability density function; unmanned aerial vehicle; video-stream;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The problem of object recognition in Unmanned Aerial Vehicle application is considered. Probabilistic Bayesian approach in object recognition is used. The accuracy of object recognition depends directly on the quality of prior data and accuracy of object parameters description. An approach for probability density estimation based of regression model is represented. Probability density functions are estimated by learning samples. The proposed approach is verified by laboratory experiment with video recording of object in rotatable platform.

引用

收藏

页码：233 / 236

页数：4

相关论文

共 50 条

[41] Simulation of Unmanned Aerial Vehicle for Mapping [J].

Turan, Burak ;

Turhan, Sultan N. ;

Pinarer, Ozgun ;

Bozkaya, Elif .

29TH IEEE CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS (SIU 2021), 2021,

[42] Spiral descent of an unmanned aerial vehicle [J].

Gomzin A.V. ;

Fesenko E.V. ;

Shataev V.G. .

Russian Aeronautics, 2009, 52 (03) :277-283

[43] Unmanned aerial vehicle navigation and mapping [J].

Lazarus, S. B. ;

Tsourdos, A. ;

Silson, P. ;

White, B. ;

Zbikowski, R. .

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, 2008, 222 (G4) :531-548

[44] METHOD FOR REAL TIME FACE RECOGNITION APPLICATION IN UNMANNED AERIAL VEHICLES [J].

Jurevicius, Romualdas ;

Goranin, Nikolaj ;

Janulevicius, Justinas ;

Nugaras, Justas ;

Suzdalev, Ivan ;

Lapusinskij, Aleksandr .

AVIATION, 2019, 23 (02) :65-70

[45] Key Technologies of the Cooperative Combat of Manned Aerial Vehicle and Unmanned Aerial Vehicle [J].

Wang, Yu ;

Chen, Hao ;

Liu, Quan ;

Huang, Jian .

ADVANCES IN GUIDANCE, NAVIGATION AND CONTROL, 2023, 845 :671-679

[46] IMPROVING POWER SPECTRAL DENSITY ESTIMATION OF UNMANNED AERIAL VEHICLE ROTOR NOISE BY LEARNING FROM NON-ACOUSTIC INFORMATION [J].

Yen, Benjamin ;

Hioka, Yusuke ;

Mace, Brian .

2018 16TH INTERNATIONAL WORKSHOP ON ACOUSTIC SIGNAL ENHANCEMENT (IWAENC), 2018, :545-549

[47] A SYSTEMATIC REVIEW OF UNMANNED AERIAL VEHICLE APPLICATION AREAS AND TECHNOLOGIES IN THE AEC DOMAIN [J].

Albeaino, Gilles ;

Gheisari, Masoud ;

Franz, Bryan W. .

JOURNAL OF INFORMATION TECHNOLOGY IN CONSTRUCTION, 2019, 24 :381-405

[48] Design and Application of an Airborne Radioactivity Survey System Based on Unmanned Aerial Vehicle [J].

Qin, Guoxiu ;

Yang, Qinghua ;

Cui, Jiarui ;

Pan, Honggang ;

Pan, Liangliang ;

Li, Fan .

NUCLEAR TECHNOLOGY, 2023, 209 (05) :707-715

[49] A systematic review of unmanned aerial vehicle application areas and technologies in the AEC domain [J].

Albeaino G. ;

Gheisari M. ;

Franz B.W. .

Journal of Information Technology in Construction, 2019, 24 :381-405

[50] An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images [J].

Qin, Rongjun .

REMOTE SENSING, 2014, 6 (09) :7911-7932

← 1 2 3 4 5 →