Egomotion compensation and moving objects detection algorithm on GPU

Juan Gómez-Luna, Holger Endt, Walter Stechele, José María González-Linares, José Ignacio Benavides, Nicolás Guil

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

2 Scopus citations

Abstract

Moving objects detection algorithms need real-time processing for diverse applications such as rescue robots and driving assistance. Moreover, in these applications, they should deal with strong levels of egomotion, which limit the reliability of most of the existing techniques. These requirements make them very compute intensive and memory bound, what enforces the use of hardware acceleration, such as FPGA or GPU. In this work, a GPU implementation of an optical flow based moving objects detection algorithm is presented. This algorithm is applicable in scenarios with weak and strong egomotion, thanks to egomotion compensation and two alternative detection methods. Our implementation includes novel approaches on GPU to widely-used techniques as RANSAC and region growing. It also solves image processing parallelization problems, as divergent execution paths, by using compaction and sorting primitives, with a significant impact on performance. Finally, our implementation has been compared to a previous FPGA implementation. From the performance point of view, results on the newest GPUs clearly outperform the FPGA.

Original languageEnglish
Title of host publicationApplications, Tools and Techniques on the Road to Exascale Computing
PublisherIOS Press BV
Pages183-190
Number of pages8
ISBN (Print)9781614990406
DOIs
StatePublished - 2012

Publication series

NameAdvances in Parallel Computing
Volume22
ISSN (Print)0927-5452

Keywords

  • Egomotion compensation
  • GPU
  • Moving objects detection
  • RANSAC
  • Region growing

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