Research Article
Design and Implementation of Pedestrian Detection System
@INPROCEEDINGS{10.1007/978-3-030-19086-6_10, author={Hengfeng Fu and Zhaoyue Zhang and Yongfei Zhang and Yun Lin}, title={Design and Implementation of Pedestrian Detection System}, proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings}, proceedings_a={ADHIP}, year={2019}, month={5}, keywords={Pedestrian detection Faster RCNN Target detection Deep learning}, doi={10.1007/978-3-030-19086-6_10} }- Hengfeng Fu
Zhaoyue Zhang
Yongfei Zhang
Yun Lin
Year: 2019
Design and Implementation of Pedestrian Detection System
ADHIP
Springer
DOI: 10.1007/978-3-030-19086-6_10
Abstract
With the popularization of self-driving cars and the rapid development of intelligent transportation, pedestrian detection shows more and more extensive application scenarios in daily life, which have higher and higher application values. It also raises more and more interest from academic community. Pedestrian detection is fundamental in many human-oriented tasks, including trajectory tracking of people, recognition of pedestrian gait, and autopilot recognition of pedestrians to take appropriate response measures. In this context, this paper studies the design and implementation of a pedestrian detection system. The pedestrian detection system of this article is mainly composed of two parts. The first part is a pedestrian detector based on deep learning, and the second part is a graphical interface that interacts with the user. The former part mainly uses the Faster R-RCNN learning model, which can use convolutional neural networks to learn features from the data and extract the features of the image. It can also search the image through RPN network for areas where the target is located and then classify them. In this paper, a complete pedestrian detection system is implemented on the basis of deep learning framework Caffe. Experiments show that the system has high recognition rate and fast recognition speed in real world.