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Pedestrian Detection based on Faster R-CNN

Volume 15, Number 7, July 2019, pp. 1792-1801
DOI: 10.23940/ijpe.19.07.p5.17921801

Shuang Liua, Xing Cuia, Jiayi Lia, Hui Yanga, and Niko Lukačb

aSchool of Computer Science and Engineering, Dalian Minzu University, Dalian, 116605, China
bFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SI-2000, Slovenia

 

(Submitted on December 10, 2018; Revised on January 12, 2019; Accepted on February 8, 2019)

Abstract:

Pedestrian detection has a wide range of applications, such as intelligent assisted driving, intelligent monitoring, pedestrian analysis, and intelligent robotics. Therefore, it has been the focus of research on target detection applications. In this paper, the Faster R-CNN target detection model is combined with the convolutional neural networks VGG16 and ResNet101 respectively, and the deep convolutional neural network is used to extract the image features. By adjusting the structure and parameters of Faster R-CNN's RPN, the multi-scale problem existing in the pedestrian detection process is solved to some extent. The experimental results compare the detection ability of the two schemes on the INRIA pedestrian dataset. The resulting model is migrated and validated on the Pascal Voc2007 dataset.

 

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