Road-sign detection and tracking

Chiung Yao Fang, Sei Wang Chen, Chiou Shann Fuh

Research output: Contribution to journalArticle

223 Citations (Scopus)

Abstract

In a visual driver-assistance system road-sign detection and tracking is one of the major tasks. This study describes an approach to detecting and tracking road signs appearing in complex traffic scenes. In the detection phase, two neural networks are developed to extract color and shape features of traffic signs from the input scenes images. Traffic signs are then located in the images based on the extracted features. This process is primarily conceptualized in terms of fuzzy-set discipline. In the tracking phase, traffic signs located in the previous phase are tracked through image sequences using a Kalman filter. The experimental results demonstrate that the proposed method performs well in both detecting and tracking road signs present in complex scenes and in various weather and illumination conditions.

Original languageEnglish
Pages (from-to)1329-1341
Number of pages13
JournalIEEE Transactions on Vehicular Technology
Volume52
Issue number5
DOIs
Publication statusPublished - 2003 Sep 1

Fingerprint

Traffic signs
Traffic
Fuzzy sets
Kalman filters
Lighting
Color
Neural networks
Driver Assistance
Shape Feature
Image Sequence
Weather
Kalman Filter
Fuzzy Sets
Illumination
Neural Networks
Experimental Results
Demonstrate

Keywords

  • (HSI) color model
  • Fuzzy integration
  • Hue
  • Intensity
  • Kalman filter
  • Neural networks
  • Road-sign detection and tracking
  • Saturation

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

Road-sign detection and tracking. / Fang, Chiung Yao; Chen, Sei Wang; Fuh, Chiou Shann.

In: IEEE Transactions on Vehicular Technology, Vol. 52, No. 5, 01.09.2003, p. 1329-1341.

Research output: Contribution to journalArticle

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