Automatic Segmentation of Dynamic Objects from an Image Pair

TL;DR

This paper presents a novel method for automatically segmenting dynamic objects from a pair of images using dense correspondences, saliency, and computational geometry, effectively handling large motions.

Contribution

The paper introduces a new top-down segmentation approach leveraging geometric techniques to improve dynamic object segmentation from image pairs.

Findings

Effective segmentation of large motions

Good accuracy compared to manual ground truth

Applicable to various scene types

Abstract

Automatic segmentation of objects from a single image is a challenging problem which generally requires training on large number of images. We consider the problem of automatically segmenting only the dynamic objects from a given pair of images of a scene captured from different positions. We exploit dense correspondences along with saliency measures in order to first localize the interest points on the dynamic objects from the two images. We propose a novel approach based on techniques from computational geometry in order to automatically segment the dynamic objects from both the images using a top-down segmentation strategy. We discuss how the proposed approach is unique in novelty compared to other state-of-the-art segmentation algorithms. We show that the proposed approach for segmentation is efficient in handling large motions and is able to achieve very good segmentation of the objects for different scenes. We analyse the results with respect to the manually marked ground truth segmentation masks created using our own dataset and provide key observations in order to improve the work in future.