Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV.

de Oliveira, Diulhio Candido; Wehrmeister, Marco Aurelio

de Oliveira, Diulhio Candido; Wehrmeister, Marco Aurelio.

Affiliation

de Oliveira DC; Computing Systems Engineering Laboratory (LESC), Federal University of Technology-Parana (UTFPR), Curitiba 80230-901, Brazil. diulhio@alunos.utfpr.edu.br.
Wehrmeister MA; Computing Systems Engineering Laboratory (LESC), Federal University of Technology-Parana (UTFPR), Curitiba 80230-901, Brazil. wehrmeister@utfpr.edu.br.

Sensors (Basel) ; 18(7)2018 Jul 12.

Article in En | MEDLINE | ID: mdl-30002290

Key words

Unmanned Aerial Vehicle (UAV); aerial images; convolutional neural network; deep learning; pattern recognition system; pedestrian detection; performance assessment; thermal camera

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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Health_economic_evaluation Language: En Journal: Sensors (Basel) Year: 2018 Document type: Article Affiliation country: Brazil Country of publication: Switzerland

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