Background model initialization is commonly the first step of the background subtraction process. In practice, several challenges appear and perturb this process such as dynamic background, bootstrapping, illumination changes, noise image, etc. In this context, this work aims to investigate the background model initialization as a matrix completion problem. Thus, we consider the image sequence (or video) as a partially observed matrix. First, a simple joint motion-detection and frame-selection operation is done. The redundant frames are eliminated , and the moving regions are represented by zeros in our observation matrix. The second stage involves evaluating nine popular matrix completion algorithms with the Scene Background Initialization (SBI) data set, and analyze them with respect to the background model challenges. The experimental results shows the good performance of LRGeomCG [17] method over its direct competitors.