Total Variation Minimization and Graph Cuts for Moving Objects Segmentation

TL;DR

This paper presents a novel approach to video segmentation by applying total variation minimization and graph cuts, effectively distinguishing moving objects from static background using optical flow and edge-based weighting.

Contribution

It introduces a new shape optimization model using weighted total variation and adapts graph cut techniques for global minimum solutions in video segmentation.

Findings

Effective separation of moving objects using optical flow norms.

The proposed methods achieve global optimality in segmentation.

Application of TV regularization enhances edge alignment in segmentation results.

Abstract

In this paper, we are interested in the application to video segmentation of the discrete shape optimization problem involving the shape weighted perimeter and an additional term depending on a parameter. Based on recent works and in particular the one of Darbon and Sigelle, we justify the equivalence of the shape optimization problem and a weighted total variation regularization. For solving this problem, we adapt the projection algorithm proposed recently for solving the basic TV regularization problem. Another solution to the shape optimization investigated here is the graph cut technique. Both methods have the advantage to lead to a global minimum. Since we can distinguish moving objects from static elements of a scene by analyzing norm of the optical flow vectors, we choose the optical flow norm as initial data. In order to have the contour as close as possible to an edge in the image, we use a classical edge detector function as the weight of the weighted total variation. This model has been used in one of our former works. We also apply the same methods to a video segmentation model used by Jehan-Besson, Barlaud and Aubert. In this case, only standard perimeter is incorporated in the shape functional. We also propose another way for finding moving objects by using an a contrario detection of objects on the image obtained by solving the Rudin-Osher-Fatemi Total Variation regularization problem.We can notice the segmentation can be associated to a level set in the former methods.