Detection of pedestrians in road context for intelligent vehicles and advanced driver assistance systems

Pedestrian detection is one of the key issues of the intelligent vehicles and advanced driver assistance systems (ADAS) used in the daily urban traffic. This paper addresses a system designed for finding the pedestrians in the road context, which can enhance the pedestrian detection performance based on the contextual correlations. More specifically, stereo vision is employed to seek the free road space based on a Markov Random Field (MRF). Such information is then used for correlation with the pedestrian detection procedure, which is based on a deformable part-based model with histogram of oriented gradient (HOG) features. Experimental results in various typical but challenging scenarios show the effectiveness of the proposed system.

• Record URL:
  https://doi.org/10.1109/ITSC.2013.6728389
• Availability:
  • Find a library where document is available. Order URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6712176
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Corporate Authors:

  Institute of Electrical and Electronics Engineers (IEEE)

  3 Park Avenue, 17th Floor
  New York, NY  United States  10016-5997
• Authors:
  • Guo, Chunzhao
  • Meguro, J
  • Kojima, Y
  • Naito, T
• Conference:
  • 16th International IEEE Conference on Intelligent Transportation Systems (ITSC)
  • Location: The Hague , Netherlands
  • Date: 2013-10-6 to 2013-10-9
• Publication Date: 2013-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1161-1166
• Monograph Title: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

Subject/Index Terms

• TRT Terms: Detection and identification; Driver support systems; Intelligent vehicles; Pedestrians
• Uncontrolled Terms: Markov random fields; Stereo vision
• Subject Areas: Design; Highways; Vehicles and Equipment; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01563067
• Record Type: Publication
• ISBN: 9781479929146
• Files: TRIS
• Created Date: May 16 2015 4:08PM