Robust vehicle edge detection by cross filter method

Katy Po Ki Tang, Henry Y T NGAN

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


In visual surveillance, vehicle tracking and identification is very popular and applied in many applications such as traffic incident detection, traffic control and management. Edge detection is the key to the success of vehicle tracking and identification. Edge detection is to identify edge locations or geometrical shape changes in term of pixel value along a boundary of two regions in an image. This paper aims to investigate different edge detection methods and introduce a Cross Filter (CF) method, with a two-phase filtering approach, for vehicle images in a given database. First, four classical edge detectors namely the Canny detector, Prewitt detector, Roberts detector and Sobel detector are tested on the vehicle images. The Canny detected image is found to offer the best performance in Phase 1. In Phase 2, the robust CF, based on a spatial relationship of intensity change on edges, is applied on the Canny detected image as a second filtering process. Visual and numerical comparisons among the classical edge detectors and CF detector are also given. The average DSR of the proposed CF method on 10 vehicle images is 95.57%.

Original languageEnglish
Article number7041898
JournalProceedings - Applied Imagery Pattern Recognition Workshop
Issue numberFebruary
Publication statusPublished - 12 Feb 2015
Event2014 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2014 - Washington, United States
Duration: 14 Oct 201416 Oct 2014

Scopus Subject Areas

  • Engineering(all)

User-Defined Keywords

  • Canny detector
  • cross filter
  • Edge detection
  • Prewitt detector
  • Roberts detector
  • Sobel detector
  • vehicle images


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