Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network

Jiasong Zhu, Ke Sun, Sen Jia, Qingquan Li, Xianxu Hou, Weidong Lin, Bozhi Liu, Guoping Qiu
2018 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This paper presents an advanced urban traffic density estimation solution using the latest deep learning techniques to intelligently process ultra high resolution traffic videos taken from an Unmanned Aerial Vehicle (UAV). We first capture nearly an hour-long ultra high resolution traffic video at 5 busy road intersections of a modern megacity by flying an UAV during the rush hours. We then randomly sampled over 17K 512x512 pixel image patches from the video frames and manually annotated over
more » ... K vehicles to form a dataset for this research which will also be made available to the research community for research purposes. Our innovative urban traffics analysis solution consists of advanced deep neural network 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 deep neural network based techniques and that deep learning techniques are more effective than traditional computer vision techniques in traffic video analysis. We will also show that ultra high resolution video provides more information which enables more accurate vehicle detection and recognition than lower resolution contents. This paper not only demonstrates the advantages of using the latest technological advancements (ultra high resolution video and UAV) but also provides an advanced deep neural network based solution for exploiting these technological advancements for urban traffic density estimation.
doi:10.1109/jstars.2018.2879368 fatcat:3adqodw6uvdzdhbz7i4tua2e7q