Traffic measurement and congestion detection based on real-time highway video data

Erik Sonnleitner, Oliver Barth, Alexander Palmanshofer, Marc Kurz

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station-and bridge-cameras and manage to keep the error rate of lost vehicles under 10%.

Original languageEnglish
Article number6270
JournalApplied Sciences (Switzerland)
Issue number18
Publication statusPublished - Sept 2020


  • Computer vision
  • Congestion detection
  • Machine learning
  • Road cameras
  • Traffic analysis
  • Vehicular tracking


Dive into the research topics of 'Traffic measurement and congestion detection based on real-time highway video data'. Together they form a unique fingerprint.

Cite this