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.
Bibliographical noteFunding Information:
The authors are grateful to FAPESP grants #2013/20387-7 and #2014/16250-9, CNPq grants #303182/2011-3, #470571/2013-6, and #306166/2014-3, and Universidad de los Andes FAI grant #05/2013.
© 2015, Pereira et al.; licensee Springer.
- Evolutionary algorithms
- Optical flow methods
- Optimization methods