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A coarse-to-fine approach for vehicles detection from aerial images

  • Long Chen*
  • , Zhiguo Jiang
  • , Junli Yang
  • , Yibing Ma
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Vehicles detection in aerial images has a wide range of applications for visual surveillance. This paper introduces a framework for robust on-road vehicle detection. A passively trained framework system is built using conventional supervised learning. The strategy which is proposed for detecting vehicles is From-coarse-to-fine. In the first step, Road is segmented with LSD algorithm to narrow the area which will be detected. AdaBoost based algorithm is used for coarse detection. SVM is used to reduce false rates. Experimental results show that this framework yields a efficient and robust on-board vehicle detection system with high precision and low false rates.

Original languageEnglish
Title of host publicationProceedings of International Conference on Computer Vision in Remote Sensing, CVRS 2012
Pages221-225
Number of pages5
DOIs
StatePublished - 2012
Event2012 International Conference on Computer Vision in Remote Sensing, CVRS 2012 - Xiamen, China
Duration: 16 Dec 201218 Dec 2012

Publication series

NameProceedings of International Conference on Computer Vision in Remote Sensing, CVRS 2012

Conference

Conference2012 International Conference on Computer Vision in Remote Sensing, CVRS 2012
Country/TerritoryChina
CityXiamen
Period16/12/1218/12/12

Keywords

  • LSD
  • SVM
  • adaboost
  • vehicles Detecting

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