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Urban traffic density estimation based on ultrahigh-resolution UAV video and deep neural network

Zhu, Jiasong; Sun, Ke; Jia, Sen; Li, Qingquan; Hou, Xianxu; Lin, Weidong; Liu, Bozhi; Qiu, Guoping

Authors

Jiasong Zhu

Ke Sun

Sen Jia

Qingquan Li

Xianxu Hou

Weidong Lin

Bozhi Liu

GUOPING QIU GUOPING.QIU@NOTTINGHAM.AC.UK
Professor of Visual Informationprocessing



Abstract

This paper presents an advanced urban traffic density estimation solution using the latest deep learning techniques to intelligently process ultrahigh-resolution traffic videos taken from an unmanned aerial vehicle (UAV). We first capture nearly an hour-long ultrahigh-resolution traffic video at five busy road intersections of a modern megacity by flying a UAV during the rush hours. We then randomly sampled over 17 K 512×512 pixel image patches from the video frames and manually annotated over 64 K vehicles to form a dataset for this paper, which will also be made available to the research community for research purposes. Our innovative urban traffics analysis solution consists of an advanced deep neural network (DNN) based vehicle detection and localization, type (car, bus, and truck) recognition, tracking, and vehicle counting over time. We will present extensive experimental results to demonstrate the effectiveness of our solution. We will show that our enhanced single shot multibox detector (Enhanced-SSD) outperforms other DNN-based techniques and that deep learning techniques are more effective than traditional computer vision techniques in traffic video analysis. We will also show that ultrahigh-resolution video provides more information that enables more accurate vehicle detection and recognition than lower resolution contents. This paper not only demonstrates the advantages of using the latest technological advancements (ultrahigh-resolution video and UAV), but also provides an advanced DNN-based solution for exploiting these technological advancements for urban traffic density estimation.

Journal Article Type Article
Publication Date 2018-12
Journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Print ISSN 1939-1404
Electronic ISSN 2151-1535
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 11
Issue 12
Pages 4968-4981
APA6 Citation Zhu, J., Sun, K., Jia, S., Li, Q., Hou, X., Lin, W., …Qiu, G. (2018). Urban traffic density estimation based on ultrahigh-resolution UAV video and deep neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12), 4968-4981. doi:10.1109/jstars.2018.2879368
DOI https://doi.org/10.1109/jstars.2018.2879368
Keywords Deep neural networks (DNNs), road traffic monitoring, traffic density estimation, unmanned aerial vehicle (UAV), vehicle counting, vehicle detection, vehicle tracking
Publisher URL https://ieeexplore.ieee.org/abstract/document/8536405

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