In the field of video image processing, moving target detection remains a hot topic. To address the limitations of existing methods in complex environments, This paper proposes a novel TRPCA model based on Tensor Singular Value Decomposition (T-SVD), incorporating the advantages of side information. Firstly, by imposing \(\gamma \) -norm constraints, the method incorporates feature side information into the background component processing, addresses the over-penalization issue caused by the nuclear norm in traditional RPCA. Secondly, for the foreground part, \(L_{1,1,2}\) norm and tensor total variation (TTV) regularization constraints are applied to enhance the model’s sensitivity to tubal sparsity and spatiotemporal continuity, effectively reducing the interference of dynamic backgrounds on foreground extraction. To solve this model, we employ the Alternating Direction Method of Multipliers (ADMM). Extensive experiments on the datasets CDnet2014 and LASIESTA demonstrate that the proposed method achieves optimal or near-optimal performance in terms of F-measure for the majority of cases, highlighting its superiority in foreground detection precision.
