In view of the fact that the detection of moving targets in real-time video are greatly affected by weather conditions. Herein, a video sequence moving target detection algorithm that combines total variation(TV) regularization and a Rank-1 constrained robust principal component analysis (RPCA) model is proposed. Using RPCA as a tool in the framework of low-rank sparse decomposition, the Rank-1 constraint is used to describe the strong low-rank of the background layer, and the TV regularization combined with the L₁ norm is used to perform the sparseness and spatial continuity of the foreground target constraints to compensate for the deficiencies of the existing RPCA model. Aiming at the proposed model, the idea of alternating iterative multiplier method combined with augmented Lagrangian multiplier method is used to optimize the objective function. Experimental results show that the proposed algorithm can not only accurately detect moving targets but also has a shorter running time, which provides a reference for real-time video detection. Compared with other similar algorithms, the proposed algorithm not only has better detection effect but also provides enhanced quantitative evaluation of F measurement value, recall rate, and accuracy rate.