Improved efficiency of road sign detection and recognition by employing kalman filter

  • Authors:

  • Usman Zakir;Amir Hussain;Liaqat Ali;Bin Luo

  • Affiliations:

  • COSPIRA Laboratory, Division of Computing Science, School of Natural Sciences, University of Stirling, Stirling, UK;COSPIRA Laboratory, Division of Computing Science, School of Natural Sciences, University of Stirling, Stirling, UK;COSPIRA Laboratory, Division of Computing Science, School of Natural Sciences, University of Stirling, Stirling, UK;Intelligent Computing and Signal Processing, Anhui University, Hefei, Anhui, China

  • Venue:
  • BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
  • Year:
  • 2013

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Abstract

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.