Overcoming Occlusion in the Automotive Environment - A Review

Shane Gilroy, Edward Jones, Martin Glavin

Research output: Contribution to journalReview articlepeer-review

72 Citations (Scopus)

Abstract

Accurate and consistent vulnerable road user detection remains one of the most challenging perception tasks for autonomous vehicles. One of the most complex outstanding issues is partial occlusion, where a sensor has only a partial view of the target object due to a foreground object that partially obscures the target. A review of occlusion detection and handling solutions for the automotive environment is presented by this research. This article first discusses object detection by the human visual system, provides an overview of occlusion reasoning in computer vision, presents a summary of occlusion handling strategies in pedestrian, vehicle and object detection applications in the automotive environment. A selection of the remaining challenges to achieving the required level of object detection performance for safe autonomous driving are also discussed.

Original languageEnglish
Article number8928542
Pages (from-to)23-35
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Volume22
Issue number1
DOIs
Publication statusPublished - Jan 2021

Keywords

  • Occlusion handling
  • autonomous vehicles
  • cyclist detection
  • occlusion benchmarking
  • pedestrian detection
  • vehicle detection

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