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المؤلفAbdeljaber, Osama
المؤلفYounis, Adel
المؤلفAlhajyaseen, Wael
تاريخ الإتاحة2020-09-21T08:05:52Z
تاريخ النشر2020
اسم المنشورArabian Journal for Science and Engineering
الاقتباسAbdeljaber, O., Younis, A., & Alhajyaseen, W. (2020). Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks. Arabian Journal for Science and Engineering, 1-15. https://doi.org/10.1007/s13369-020-04546-y
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/s13369-020-04546-y
معرّف المصادر الموحدhttp://hdl.handle.net/10576/16211
الملخصThis paper aims at developing a convolutional neural network (CNN)-based tool that can automatically detect the left-turning vehicles (right-hand traffic rule) at signalized intersections and extract their trajectories from a recorded video. The proposed tool uses a region-based CNN trained over a limited number of video frames to detect moving vehicles. Kalman filters are then used to track the detected vehicles and extract their trajectories. The proposed tool achieved an acceptable accuracy level when verified against the manually extracted trajectories, with an average error of 16.5 cm. Furthermore, the trajectories extracted using the proposed vehicle tracking method were used to demonstrate the applicability of the minimum-jerk principle to reproduce variations in the vehicles' paths. The effort presented in this paper can be regarded as a way forward toward maximizing the potential use of deep learning in traffic safety applications.
اللغةen
الناشرSpringerLink
الموضوعTraffic safety
Signalized intersections
Turning vehicle trajectories
Convolutional neural networks
Minimum-jerk principle
العنوانExtraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks
النوعArticle
الصفحات1-15
dc.accessType Open Access


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