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 \(L_1\)-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.

背景减法是一项非常重要的视频处理任务，旨在将前景与视频分离，以使后处理任务高效且相对容易。到目前为止，已经提出了几种不同的技术来完成该任务，但是对于在前景和背景上都有变化的视频，它们中的大多数都不能很好地执行。在本文中，提出了一种新颖的背景扣除技术，旨在逐步拟合从\（L_1 \）获得的背景的特定子空间。-低秩矩阵分解使用循环加权中值算法和前景的噪声的高斯混合的一定分布。应用期望最大化算法来优化高斯混合模型。此外，为了消除相机抖动影响，需要使用仿射变换算符来对齐连续的帧。最后，使用二次采样技术可以提高提出的方法的有效性，该子采样技术可以加速提出的方法以平均每秒执行250帧以上的速度，同时在准确性方面保持良好的性能。将所提方法的性能与其他最新方法进行了比较，得出的结论是，所提方法在F度量和计算复杂度方面表现良好。