Improved efficiency of road sign detection and recognition by employing Kalman filter: 6th International Conference, BICS 2013, Beijing, China, June 9-11, 2013. Proceedings

Usman Zakir, Amir Hussain, Liaqat Ali, Bin Luo

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)


This paper describes an efficient approach towards road sign detection, and recognition. The proposed system is divided into three sections namely: Road Sign Detection where Colour Segmentation of the road traffic signs is carried out using HSV colour space considering varying lighting conditions and Shape Classification is achieved by using Contourlet Transform, considering possible occlusion and rotation of the candidate signs. Road Sign Tracking is introduced by using Kalman Filter where object of interest is tracked until it appears in the scene. Finally, Road Sign Recognition is carried out on successfully detected and tracked road sign by using features of a Local Energy based Shape Histogram (LESH). Experiments are carried out on 15 distinctive classes of road signs to justify that the algorithm described in this paper is robust enough to detect, track and recognize road signs under varying weather, occlusion, rotation and scaling conditions using video stream.
Original languageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems
EditorsDerong Liu, Cesare Alippi, Dongbin Zhao, Amir Hussain
Place of PublicationBerlin
Number of pages9
Publication statusPublished - 11 Jun 2013
Event6th International Conference, BICS 2013 - Beijing, China
Duration: 9 Jun 201311 Jun 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


Conference6th International Conference, BICS 2013


  • SVM
  • road signs
  • HSV
  • contourlet transform
  • LESH
  • colour segmentation
  • autonomous vehicles
  • kalman filter

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