An Improved Target Detection and Traffic Parameter Calculation Method based on YOLO with a Monocular Camera

YOLO, which is one of the deep learning target detection methods, has been used in detecting traffic targets in real-time on regular devices under general light and medium traffic density. Compared with other deep learning methods like R-CNN, it was reported more efficient in computing, but less accurate in bounding box detection. In order to improve the detecting performance, this paper builds a hybrid method which combines frame difference (FD) method with YOLO. The result shows significant improvement in accuracy of bounding boxes, with little effect on detecting speed. This paper also proposes a model to calculate the speed and distance of targets in real-time based on a fixed monocular camera. The methods introduced in this paper can be used to warn drivers based on the traffic surveillance systems at intersections, or to help drivers avoid dangerous driving behaviors through on-board-devices.

• Record URL:
  https://doi.org/10.1061/9780784482292.490
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784482292
• Supplemental Notes:
  • © 2019 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Tian, Shan
  • Yu, Heng
  • Yang, Zi
  • Jing, Xiao
  • Zhang, Zuo
  • Shi, Mengkai
  • Wang, Yizhi
• Conference:
  • 19th COTA International Conference of Transportation Professionals
  • Location: Nanjing , China
  • Date: 2019-7-6 to 2019-7-8
• Publication Date: 2019-7

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 5696-5708
• Monograph Title: CICTP 2019: Transportation in China—Connecting the World

Subject/Index Terms

• TRT Terms: Cameras; Proximity detectors; Traffic estimation
• Identifier Terms: YOLO
• Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01712059
• Record Type: Publication
• ISBN: 9780784482292
• Files: TRIS, ASCE
• Created Date: Jul 22 2019 8:00PM