Jointly optical flow and occlusion estimation for images with large displacements

Abstract

This paper deals with motion estimation of objects in a video sequence. This problem is known as optical flow estimation. Traditional models to estimate it fail in presence of occlusions and non-uniform illumination. To tackle these problems we propose a variational model to jointly estimate optical flow and occlusions. The proposed model is able to deal with the usual drawback of variational methods in dealing with large displacements of objects in the scene which are larger than the object itself. The addition of a term that balances gradient and intensities increases the robustness to illumination changes of the proposed model. The inclusion of a supplementary matching obtained by exhaustive search in specific locations helps to follow large displacements.

This paper deals with motion estimation of objects in a video sequence. This problem is known as optical flow estimation. Traditional models to estimate it fail in presence of occlusions and non-uniform illumination. To tackle these problems we propose a variational model to jointly estimate optical flow and occlusions. The proposed model is able to deal with the usual drawback of variational methods in dealing with large displacements of objects in the scene which are larger than the object itself. The addition of a term that balances gradient and intensities increases the robustness to illumination changes of the proposed model. The inclusion of a supplementary matching obtained by exhaustive search in specific locations helps to follow large displacements.