Background subtraction based on Local Shape

TL;DR

This paper introduces a background subtraction method leveraging local shape analysis via self-similarity descriptors to improve foreground detection in images.

Contribution

It proposes a novel background modeling technique based on local shape changes, enhancing change detection accuracy.

Findings

Foregrounds are complete but include shadows.

The method effectively distinguishes background from moving objects.

Results demonstrate promising detection performance.

Abstract

We present a novel approach to background subtraction that is based on the local shape of small image regions. In our approach, an image region centered on a pixel is mod-eled using the local self-similarity descriptor. We aim at obtaining a reliable change detection based on local shape change in an image when foreground objects are moving. The method first builds a background model and compares the local self-similarities between the background model and the subsequent frames to distinguish background and foreground objects. Post-processing is then used to refine the boundaries of moving objects. Results show that this approach is promising as the foregrounds obtained are com-plete, although they often include shadows.