Video Segmentation via Diffusion Bases

TL;DR

This paper introduces a novel video segmentation algorithm using diffusion bases to effectively distinguish moving objects from backgrounds in static and dynamic scenes, enhancing robustness to illumination changes and background dynamics.

Contribution

The paper proposes a new diffusion bases-based method for background subtraction that adapts to both static and dynamic backgrounds, improving robustness and responsiveness.

Findings

Effective separation of foreground and background in various scenes

Robust to illumination changes and non-stationary background objects

Two algorithm versions for static and dynamic backgrounds

Abstract

Identifying moving objects in a video sequence, which is produced by a static camera, is a fundamental and critical task in many computer-vision applications. A common approach performs background subtraction, which identifies moving objects as the portion of a video frame that differs significantly from a background model. A good background subtraction algorithm has to be robust to changes in the illumination and it should avoid detecting non-stationary background objects such as moving leaves, rain, snow, and shadows. In addition, the internal background model should quickly respond to changes in background such as objects that start to move or stop. We present a new algorithm for video segmentation that processes the input video sequence as a 3D matrix where the third axis is the time domain. Our approach identifies the background by reducing the input dimension using the \emph{diffusion bases} methodology. Furthermore, we describe an iterative method for extracting and deleting the background. The algorithm has two versions and thus covers the complete range of backgrounds: one for scenes with static backgrounds and the other for scenes with dynamic (moving) backgrounds.