A comprehensive test framework to determine the spatial performance of camera-based vehicle detection algorithms

Damien Dooley, Brian McGinley, Liam Kilmartin, Edward Jones, Martin Glavin, Ciarán Hughes

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

Abstract

This paper proposes a performance test framework for vision based vehicle detection systems implemented on road-going vehicles. An extensive literature review outlines the evolution of test frameworks used by a number of recently published vehicle detection systems. The proposed test framework determines the effectiveness of a detection algorithm as a function of distance between the host and target vehicles. The framework assists the characterisation of an algorithm's performance over the full range of distances in which a vehicle can be detected in an image. The test framework is designed for use in blind spot detection, forward collision warning and rear end collision warning applications.

Original languageEnglish
Title of host publication2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages377-382
Number of pages6
ISBN (Electronic)9781479951994
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore
Duration: 10 Dec 201412 Dec 2014

Publication series

Name2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014

Conference

Conference2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
Country/TerritorySingapore
CitySingapore
Period10/12/1412/12/14

Keywords

  • automotive vision
  • blind spot detection
  • fish-eye lens
  • forward collision warning
  • Image processing
  • rear-end collision warning

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