A detection method based on Tensor Low-Rank Representation (TLRR) and spatial-temporal sparsity decomposition was proposed to detect foreground targets in video sequences. Since foreground in video sequence has sparsity inherently besides spatially continuous and temporally continuous, this paper put forward spatial-temporal sparsity-inducing norm to perform deep research on property of foreground. Original video was decomposed in tensor representation formed by tensor low-rank representation method, line information and column information of original data were fully used, and two-stage decomposition of original background and foreground was generalized to three-stage decomposition of background, foreground and noises. Optimization solution was performed with Inexact Augmented Lagrange Multiplier (IALM) method.Verification and comparison experiment was established, and further research experiment was performed to research how ρ affecting performance of algorithm. Experimental results show that the method can detect moving foreground in video effectively and improve accuracy when compared with existing methods.