A Bayesian approach to traffic light detection and mapping

Siavash Hosseinyalamdary*, Alper Yilmaz

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
16 Downloads (Pure)


Automatic traffic light detection and mapping is an open research problem. The traffic lights vary in color, shape, geolocation, activation pattern, and installation which complicate their automated detection. In addition, the image of the traffic lights may be noisy, overexposed, underexposed, or occluded. In order to address this problem, we propose a Bayesian inference framework to detect and map traffic lights. In addition to the spatio-temporal consistency constraint, traffic light characteristics such as color, shape and height is shown to further improve the accuracy of the proposed approach. The proposed approach has been evaluated on two benchmark datasets and has been shown to outperform earlier studies. The results show that the precision and recall rates for the KITTI benchmark are 95.78% and 92.95% respectively and the precision and recall rates for the LARA benchmark are 98.66% and 94.65%.

Original languageEnglish
Pages (from-to)184-192
Number of pages9
JournalISPRS journal of photogrammetry and remote sensing
Publication statusPublished - 1 Mar 2017


  • Bayesian inference
  • Conic section geometry
  • Spatio-temporal consistency
  • Traffic light detection and mapping
  • n/a OA procedure


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