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Journal of Applied Remote Sensing

Automatic vehicle detection based on automatic histogram-based fuzzy C-means algorithm and perceptual grouping using very high-resolution aerial imagery and road vector data
Author(s): Saman Ghaffarian; Ilgın Gökaşar
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Paper Abstract

This study presents an approach for the automatic detection of vehicles using very high-resolution images and road vector data. Initially, road vector data and aerial images are integrated to extract road regions. Then, the extracted road/street region is clustered using an automatic histogram-based fuzzy C-means algorithm, and edge pixels are detected using the Canny edge detector. In order to automatically detect vehicles, we developed a local perceptual grouping approach based on fusion of edge detection and clustering outputs. To provide the locality, an ellipse is generated using characteristics of the candidate clusters individually. Then, ratio of edge pixels to nonedge pixels in the corresponding ellipse is computed to distinguish the vehicles. Finally, a point-merging rule is conducted to merge the points that satisfy a predefined threshold and are supposed to denote the same vehicles. The experimental validation of the proposed method was carried out on six very high-resolution aerial images that illustrate two highways, two shadowed roads, a crowded narrow street, and a street in a dense urban area with crowded parked vehicles. The evaluation of the results shows that our proposed method performed 86% and 83% in overall correctness and completeness, respectively.

Paper Details

Date Published: 10 February 2016
PDF: 15 pages
J. Appl. Rem. Sens. 10(1) 015011 doi: 10.1117/1.JRS.10.015011
Published in: Journal of Applied Remote Sensing Volume 10, Issue 1
Show Author Affiliations
Saman Ghaffarian, Bogaziçi Univ. (Turkey)
Ilgın Gökaşar, Boğaziçi University (Turkey)

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