NDLI: Multi-receptive field graph convolutional neural networks for pedestrian detection

Content Provider	IET Digital Library
Author	Shen, Chao Zhao, Xiangmo Fan, Xing Lian, Xinyu Zhang, Fan Kreidieh, Abdul Rahman Liu, Zhanwen
Abstract	Although great progress has been achieved for general object detection with the advent of deep learning, there are still several limitations when applied to pedestrian detection task. In this article, the authors mainly consider the following three problems. (i) most deep learning-based methods are time-consuming and require large storage space, making them unsuitable for real-world applications; (ii) the scale of person varies greatly, which decreases the pedestrian detection performance; (iii) as pedestrians may be occluded by vehicles or other persons, it makes pedestrian detection more challenging. In response to these limitations, this article proposes a multi-receptive field-based framework for single-shot pedestrian detection. Authors’ contributions can be summarised as follows. First, to reduce the computational complexity of the detector, the authors design the multi-receptive pooling pyramid module, which reduces the computation cost of the detector and improves its performance. Next, body parts graphs are built on the learned convolutional neural networks (CNN) features, and a graph CNN is used to mine the relationships of the graph nodes. Finally, experiments are conducted on two public pedestrian detection datasets to demonstrate the effectiveness of the proposed method.
Starting Page	1319
Ending Page	1328
Page Count	10
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 9, Sep (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5618
Journal	IET Intelligent Transport Systems
Publisher Date	2019-04-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Authors Design Body Parts Graphs Combinatorial Mathematics Computer Vision And Image Processing Technique Convolutional Neural Nets Deep Learning General Object Detection Graph CNN Graph Theory Image Recognition Knowledge Engineering Technique Learned Convolutional Neural Network Feature Learning in AI Multireceptive Field Graph Convolutional Neural Network Multireceptive Field-based Framework Multireceptive Pooling Pyramid Module Neural Computing Technique Object Detection Pedestrian Pedestrian Detection Performance Pedestrian Detection Task Public Pedestrian Detection Datasets Single-shot Pedestrian Detection Traffic Engineering Computing
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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