Quaternion-based dynamic mode decomposition for background modeling in color videos

TL;DR

This paper introduces a quaternion-based dynamic mode decomposition method for background modeling in color videos, preserving color information better than traditional grayscale approaches and outperforming existing methods on benchmark datasets.

Contribution

The paper proposes a novel quaternion-based DMD that maintains color channel coupling, improving background modeling accuracy in color videos.

Findings

Q-DMD outperforms traditional DMD in background modeling tasks.

Q-DMD achieves comparable performance to state-of-the-art methods.

The approach effectively preserves color structure in video analysis.

Abstract

Scene Background Initialization (SBI) is one of the challenging problems in computer vision. Dynamic mode decomposition (DMD) is a recently proposed method to robustly decompose a video sequence into the background model and the corresponding foreground part. However, this method needs to convert the color image into the grayscale image for processing, which leads to the neglect of the coupling information between the three channels of the color image. In this study, we propose a quaternion-based DMD (Q-DMD), which extends the DMD by quaternion matrix analysis, so as to completely preserve the inherent color structure of the color image and the color video. We exploit the standard eigenvalues of the quaternion matrix to compute its spectral decomposition and calculate the corresponding Q-DMD modes and eigenvalues. The results on the publicly available benchmark datasets prove that our Q-DMD outperforms the exact DMD method, and experiment results also demonstrate that the performance of our approach is comparable to that of the state-of-the-art ones.