Matrix and tensor completion algorithms for background model initialization: A comparative evaluation

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, we investigate the background model initialization as a reconstruction problem from missing data. This problem can be formulated as a matrix or tensor completion task where the image sequence (or video) is revealed as partially observed data. In this paper, the missing entries are induced from the moving regions through a simple joint motion-detection and frame-selection operation. The redundant frames are eliminated, and the moving regions are represented by zeros in our observation model. The second stage involves evaluating twenty-three state-of-the-art algorithms comprising of thirteen matrix completion and ten tensor completion algorithms. These algorithms aim to recover the low-rank component (or background model) from partially observed data. The Scene Background Initialization data set was selected in order to evaluate this proposal with respect to the background model challenges. Our experimental results show the good performance of LRGeomCG method over its direct competitors.