Background subtraction is an important and fundamental step in video analysis and is a challenging task due to the dynamic background, bad weather, complex moving behaviors and huge amount of data in real-life applications. To address this issue, we proposed a new decomposition model based on the tensor robust principal component analysis, which makes full use of the continuity of time and the correlation of space. The Kronecker-basis-representation based tensor sparsity was introduced into the proposed model to constrain the spatio-temporal correlation of the video background and to enhance the consideration of low-rank characteristics, thus effectively reducing the interference of the dynamic background. The \({l_{1,1,2}}\) norm was used to constrain the sparseness of the spatio-temporal structure of the foreground, which strengthens the spatio-temporal continuity and tube sparsity of the video foreground and improves the accuracy of moving objects extraction. Experiments demonstrate that, in most cases, the proposed algorithm achieves superior performance in terms of F-measure scores and visual effect of separating the foreground and background of the video compared with state-of-the-art methods.

背景减去是视频分析中重要且基本的步骤，由于动态背景，恶劣的天气，复杂的移动行为以及现实应用中的大量数据，这是一项具有挑战性的任务。为了解决这个问题，我们提出了一个基于张量鲁棒主成分分析的新分解模型，该模型充分利用了时间的连续性和空间的相关性。将基于Kronecker基表示的张量稀疏性引入到所提出的模型中，以约束视频背景的时空相关性并增强对低秩特征的考虑，从而有效地减少了动态背景的干扰。的\（{L_ {1,1,2}} \）规范用于约束前景的时空结构的稀疏性，从而增强了视频前景的时空连续性和管稀疏性，并提高了运动对象提取的准确性。实验表明，在大多数情况下，与最新方法相比，该算法在F量度得分和分离视频前景和背景的视觉效果方面均具有出色的性能。