Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments

Danillo Roberto Pereira, José Delpiano, João Paulo Papa

Resultado de la investigación: Contribución a una conferenciaArtículo

2 Citas (Scopus)

Resumen

Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.
Idioma originalInglés estadounidense
Páginas125-132
Número de páginas8
DOI
EstadoPublicada - 1 ene 2014
EventoBrazilian Symposium of Computer Graphic and Image Processing -
Duración: 1 ene 2014 → …

Conferencia

ConferenciaBrazilian Symposium of Computer Graphic and Image Processing
Período1/01/14 → …

Palabras clave

  • Evolutionary Optimization Methods
  • Optical Flow
  • Social-Spider Optimization

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