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

• URL: http://arxiv.org/abs/2112.13982v1
• Date: Tue, 28 Dec 2021 03:35:39 GMT
• Title: Quaternion-based dynamic mode decomposition for background modeling in color videos
• Authors: Juan Han, Kit Ian Kou, Jifei Miao
• Abstract summary: Dynamic mode decomposition (DMD) is a recently proposed method to robustly decompose a video sequence into the background model and the corresponding foreground part. We propose a quaternion-based DMD (Q-DMD), which extends the DMD by quaternion matrix analysis. We exploit the standard eigenvalues of the quaternion matrix to compute its spectral decomposition and calculate the corresponding Q-DMD modes and eigenvalues.
• Score: 1.0098114696565863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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