Singular Spectrum Analysis for Background Initialization with Spatio-Temporal RGB Color Channel Data

Abstract

In video processing, background initialization aims to obtain a scene without foreground objects. Recently, the background initialization problem has attracted the attention of researchers because of its real-world applications, such as video segmentation, computational photography, video surveillance, etc. However, the background initialization problem is still challenging because of the complex variations in illumination, intermittent motion, camera jitter, shadow, etc. This paper proposes a novel and effective background initialization method using singular spectrum analysis. Firstly, we extract the video’s color frames and split them into RGB color channels. Next, RGB color channels of the video are saved as color channel spatio-temporal data. After decomposing the color channel spatio-temporal data by singular spectrum analysis, we obtain the stable and dynamic components using different eigentriple groups. Our study indicates that the stable component contains a background image and the dynamic component includes the foreground image. Finally, the color background image is reconstructed by merging RGB color channel images obtained by reshaping the stable component data. Experimental results on the public scene background initialization databases show that our proposed method achieves a good color background image compared with state-of-the-art methods.

Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing

“…This method significantly improves the accuracy of the suggested prediction model [93]. Additionally, there are various noise components in each time series, and the pre-treatment signal approaches (i.e., Wavelet [94], Empirical Mode Decomposition [95], Singular Spectrum Analysis [96], etc.) are the most effective methods to denoise the original time series by analysing them into multiple components [88].…”