Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (7): 1792-1801.doi: 10.23940/ijpe.19.07.p5.17921801

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Pedestrian Detection based on Faster R-CNN

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

  1. a School of Computer Science and Engineering, Dalian Minzu University, Dalian, 116605, China
    b Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SI-2000, Slovenia
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  • About author:Shuang Liu received her Ph.D. in traffic information engineering and control from Dalian Maritime University. She is currently an associate professor at Dalian Minzu University.Xing Cui is currently a postgraduate candidate at Dalian Minzu University. Jiayi Li is currently a postgraduate candidate at Dalian Minzu University.Hui Yang is currently a postgraduate candidate at Dalian Minzu University.Niko Lukač obtained his Ph.D. in computer science in 2016 from Maribor University. He is currently a researcher in the faculty of Electrical Engineering and Computer Science at the University of Maribor.

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.

Key words: pedestrian detection, faster R-CNN, feature extraction, deep learning