A new approach to video-based traffic surveillance using fuzzy hybrid information inference mechanism

Bing-Fei Wu, Chih Chung Kao, Jhy Hong Juang, Yi Shiun Huang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

This study proposes a new approach to video-based traffic surveillance using a fuzzy hybrid information inference mechanism (FHIIM). The three major contributions of the proposed approach are background updating, vehicle detection with block-based segmentation, and vehicle tracking with error compensation. During background updating, small-range updating is adopted to overcome environmental changes under congested conditions. During vehicle detection, the proposed approach detects the vehicle candidates from the foreground image, and it resolves problems such as headlight effects. The tracking technique is employed to track vehicles in consecutive frames. First, the method detects edge features in congested scenes. Next, FHIIM is employed to determine the tracked vehicles. Finally, a method that compensates for error cases under congested conditions is applied to refine the tracking qualities. In our experiments, we tested scenarios both inside and outside the tunnel with three lanes. The results showed that the proposed system exhibits good performance under congested conditions.

Original languageEnglish
Article number6264098
Pages (from-to)485-491
Number of pages7
JournalIEEE Transactions on Intelligent Transportation Systems
Volume14
Issue number1
DOIs
StatePublished - 1 Jan 2013

Keywords

  • Congested condition
  • traffic surveillance
  • vehicle detection
  • vehicle tracking

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