Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network

Existing methodologies to count vehicles from a road image have depended upon both hand-crafted feature engineering and rule-based algorithms. These require many predefined thresholds to detect and track vehicles. This paper provides a supervised learning methodology that requires no such feature engineering. A deep convolutional neural network was devised to count the number of vehicles on a road segment based solely on video images. The present methodology does not regard an individual vehicle as an object to be detected separately; rather, it collectively counts the number of vehicles as a human would. The test results show that the proposed methodology outperforms existing schemes.

• Record URL:
  • https://doi.org/10.1109/TITS.2017.2732029
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Chung, Jiyong
  • Sohn, Keemin
• Publication Date: 2018-5

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 1670-1675
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 19
  • Issue Number: 5
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Computer vision; Image analysis; Machine learning; Neural networks; Traffic counting; Traffic density; Traffic surveillance; Vehicle detectors
• Uncontrolled Terms: Deep learning
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01671099
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: May 29 2018 5:18PM