Road Curb Detection and Localization With Monocular Forward-View Vehicle Camera

The authors propose a robust method for estimating road curb 3-D parameters (size, location, and orientation) using a calibrated monocular camera equipped with a fisheye lens. Automatic curb detection and localization is particularly important in the context of an advanced driver assistance system, i.e., to prevent possible collision and damage to the vehicle’s bumper during perpendicular and diagonal parking maneuvers. Combining 3-D geometric reasoning with advanced vision-based detection methods, their approach is able to estimate the vehicle to curb distance in real time with a mean accuracy of more than 90%, as well as its orientation, height, and depth. Their approach consists of two distinct components—curb detection in each individual video frame and temporal analysis. The first part is comprised of sophisticated curb edges extraction and parameterized 3-D curb template fitting. Using a few assumptions regarding the real-world geometry, they can thus retrieve the curb’s height and its relative position with respect to the moving vehicle on which the camera is mounted. Support vector machine classifier fed with histograms of oriented gradients is used for appearance-based filtering out outliers. In the second part, the detected curb regions are tracked in the temporal domain, so as to perform a second pass of false positives rejection. They have validated their approach on a newly collected database of 11 videos under different conditions. The authors have used point-wise LIDAR measurements and manual exhaustive labels as a ground truth.

• Record URL:
  • https://doi.org/10.1109/TITS.2018.2878652
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2019, IEEE.
• Authors:
  • Panev, Stanislav
  • Vicente, Francisco
  • De la Torre, Fernando
  • Prinet, Véronique
• Publication Date: 2019-9

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 3568-3584
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 20
  • Issue Number: 9
  • 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: Automated vehicle control; Cameras; Computer vision; Curb side parking; Curbs; Image analysis; In vehicle sensors; Parking guidance systems; Tracking systems
• Candidate Terms: Localization
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01718406
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Sep 27 2019 12:07PM