Deep Learning Based Vehicle Tracking in Traffic Management

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-3
Year of Publication : 2019
Authors : Mohamed Shehata, Reda Abo-Alez, Farid Zaghlool, and Mohamed Taha Abou-Kreisha
  10.14445/22312803/IJCTT-V67I3P102

MLA

MLA Style: Mohamed Shehata, Reda Abo-Alez, Farid Zaghlool, and Mohamed Taha Abou-Kreisha, "Deep Learning Based Vehicle Tracking in Traffic Management" International Journal of Engineering Trends and Technology 67.3 (2019): 5-8.

APA Style: Mohamed Shehata, Reda Abo-Alez, Farid Zaghlool, and Mohamed Taha Abou-Kreisha, (2019). Deep Learning Based Vehicle Tracking in Traffic Management. International Journal of Engineering Trends and Technology, 67(3), 5-8.

Abstract
Nowadays the visual vehicle tracking system (VTS) becomes a vital part of the Intelligent Transportation System (ITS). It is the cornerstone of vehicles behavior analysis. Our methodology for developing a VTS achieves video based vehicles detection, classification and tracking. In the detection process a deep machine learning system based on faster region conventional neural network (Faster-RCNN) detector is used. In the classification process a deep machine learning system based on conventional neural network (CNN) is used. In the tracking process, motion vector estimation (MVE) algorithm is used to determine vehicles directions and positions in the video frames. Finally vehicle behaviour understanding algorithm based on vehicle trajectory implementation and vehicle speed calculation is used to manage the traffic flow. After testing the developed VTS, the results show that, 95% of the tested vehicles are precisely detected, 90% of the detected vehicles are successfully classified, and 92% of detected vehicles tracks are well generated.

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Keywords
Video Processing, Faster-RCNN, CNN, Motion Vector Estimation.