Color-Based Free-Space Segmentation Using Online Disparity-Supervised Learning

This work contributes to vision processing for Advanced Driver Assist Systems (ADAS) and intelligent vehicle applications. The authors propose a color-only stixel segmentation framework to segment traffic scenes into free, drivable space and obstacles, which has a reduced latency to improve the real-time processing capabilities. The authors' system learns color appearance models for free-space and obstacle classes in an online and self-supervised fashion. To this end, it applies a disparity-based segmentation, which can run in the background of the critical system path, either with a time delay of several frames or at a frame rate that is only a third of that of the color-based algorithm. In parallel, the most recent video frame is analyzed solely with these learned color appearance models, without an actual disparity estimate and the corresponding latency. This translates into a reduced response time from data acquisition to data analysis, which is a critical property for high-speed ADAS. The authors' evaluation on two publicly available datasets, one of which they introduce as part of this work, shows that the color-only analysis can achieve similar or even better results in difficult imaging conditions, compared to the disparity-only method. The authors' system improves the quality of the free-space analysis, while simultaneously lowering the latency and the computational load.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 906-912
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01602371
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:24PM