Abstract

At present, low-rank and sparse decomposition model has been widely used in the field of computer vision because of its excellent performance. However, the model still faces many challenges, such as being easily disturbed by dynamic background, failing to use prior information and heavy computational burden. To solve these problems, this paper proposes a novel low-rank and sparse decomposition model based on prior information, group sparsity, and nonconvex total variation. First, the rank of background matrix is fixed to 1, so singular value decomposition is no longer needed, which greatly reduces the computational burden. Secondly, the foreground target is divided into dynamic background and real foreground to reduce the interference of dynamic background. Finally, l_2,1-norm and nonconvex total variation is introduced into model to incorporate prior information of dynamic background and real foreground. The experimental results show that compared with several classical models, our model can extract the foreground target from the dynamic background more accurately, more completely and more quickly.

中文翻译：

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一种新的低秩稀疏分解模型及其在运动目标检测中的应用

摘要

目前，低秩稀疏分解模型因其优异的性能在计算机视觉领域得到了广泛的应用。然而，该模型仍面临许多挑战，例如容易受到动态背景的干扰、无法使用先验信息和计算负担过重。针对这些问题，本文提出了一种基于先验信息、组稀疏性和非凸总变异的新型低秩稀疏分解模型。首先，背景矩阵的秩固定为1，不再需要奇异值分解，大大减少了计算负担。其次，将前景目标分为动态背景和真实前景，以减少动态背景的干扰。最后，l _2,1-范数和非凸总变异被引入模型以结合动态背景和真实前景的先验信息。实验结果表明，与几种经典模型相比，我们的模型能够更准确、更完整、更快速地从动态背景中提取前景目标。