Background subtraction in videos using LRMF and CWM algorithm

Background subtraction is a substantially important video processing task that aims at separating the foreground from a video to make the post-processing tasks efficient and relatively easier. Until now, several different techniques have been proposed for this task, but most of them cannot perform well for the videos having variations in both the foreground and the background. In this paper, a novel background subtraction technique is proposed that aims at progressively fitting a particular subspace for the background that is obtained from L₁-low-rank matrix factorization using the cyclic weighted median algorithm and a certain distribution of a mixture of Gaussian of noise for the foreground. The expectation maximization algorithm is applied to optimize the Gaussian mixture model. Furthermore, to eliminate the camera jitter effects, the affine transformation operator is involved to align the successive frames. Finally, the effectiveness of the proposed method is augmented using a subsampling technique that can accelerate the proposed method to execute on an average more than 250 frames per second while maintaining good performance in terms of accuracy. The performance of the proposed method is compared with other state-of-the-art methods and it was concluded that the proposed method performs well in terms of F-measure and computational complexity.