A Lightweight Pedestrian Detection Engine with Two-Stage Low-Complexity Detection Network and Adaptive Region Focusing Technique.

Que, Luying; Zhang, Teng; Guo, Hongtao; Jia, Conghan; Gong, Yuchuan; Chang, Liang; Zhou, Jun

Que, Luying; Zhang, Teng; Guo, Hongtao; Jia, Conghan; Gong, Yuchuan; Chang, Liang; Zhou, Jun.

Afiliação

Que L; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Zhang T; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Guo H; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Jia C; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Gong Y; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Chang L; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Zhou J; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel) ; 21(17)2021 Aug 30.

Article em En | MEDLINE | ID: mdl-34502741

Assuntos

Pedestres; Humanos; Inteligência

Palavras-chave

FPGA; adaptive; lightweight; neural network; pedestrian detection

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pedestres Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

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