detection in complex environment is affected by many factors. Traditional robust principal component analysis (RPCA) fails to obtain the lowest rank representation from disturbed data. Therefore, a novel method of video denoising and object detection algorithm based on RPCA model with l₁-total variational (TV) regularization constraints is proposed. Based on RPCA, under the framework of low-rank sparse decomposition, the low-rank nature of the nuclear norm is used to model the background, and the three-dimensional TV combined with l₁ norm regularization to constrain the sparsity and spatial continuity of the foreground object, and then l₂ norm regularization is combined to constrain the noise part so as to make up for the deficiencies of the existing RPCA model. Using alternating iteration method, augmented Lagrange multiplier method is used to optimize the objective function, and the denoising and target detection in complex environment are realized. Experimental results show that the method can not only accurately detect moving objects under noise interference, but also maintain a relatively fast running speed, which provides a reference for the real-time detection of video. Compared with other similar methods, it not only has better detection effect, but also has advantages in the three indicators of quantitative evaluation.