Real-Time Traffic-Sign Recognition Using Tree Classifiers

Traffic-sign recognition (TSR) is an essential component of a driver assistance system (DAS), providing drivers with safety and precaution information. In this paper, we evaluate the performance of k-d trees, random forests, and support vector machines (SVMs) for traffic-sign classification using different-sized histogram-of-oriented-gradient (HOG) descriptors and distance transforms (DTs). We also use the Fisher's criterion and random forests for the feature selection to reduce the memory requirements and enhance the performance. We use the German Traffic Sign Recognition Benchmark (GTSRB) data set containing 43 classes and more than 50 000 images.

• Record URL:
  • https://doi.org/10.1109/TITS.2012.2225618
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Authors:
  • Zaklouta, F
  • Stanciulescu, B
• Publication Date: 2012

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 1507-1514
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 13
  • Issue Number: 4
  • 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: Classification; Detection and identification systems; Histograms; Image processing; Real time information; Traffic signs
• Subject Areas: Data and Information Technology; Highways; I70: Traffic and Transport;

Filing Info

• Accession Number: 01501386
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Dec 17 2013 9:31AM