On the optical flow model selection through metaheuristics

• Autor(a) principal: Pereira, Danillo R. [UNESP]
• Data de Publicação: 2015
• Outros Autores: Delpiano, José, Papa, João P. [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://jivp.eurasipjournals.com/content/2015/1/11; http://hdl.handle.net/11449/129416
• topic: Optimization methods; Evolutionary algorithms; Optical flow methods

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 optimization-based framework for such task based on social-spider optimization, harmony search, particle swarm optimization, and Nelder-Mead algorithm. The proposed framework employed the well-known large displacement optical flow (LDOF) approach as a basis algorithm over the Middlebury and Sintel public datasets, with promising results considering the baseline proposed by the authors of LDOF.