The traditional method of moving object detection is based on lowrank and sparse decomposition algorithm, which has the problems of foreground extraction results affected by noise and incomplete detection of moving objects. To solve these problems, one kind of new lowrank and sparse decomposition model is proposed. Not only the structural distribution characteristics of the video foreground object but also the influence of the dynamic background on the foreground extraction result are considered. The foreground is constrained by using the structured sparse norm. The motion area represented by the section is consisted of a dynamic background part and a foreground part. Then, the generalized alternating direction multiplier method is used to solve the proposed model, and the complexity of the algorithm is analyzed. Finally, simulation experiment is carried out and applied to moving object detection. Experimental results show that the proposed method is more stable and effective than other moving object detection methods based on lowrank and sparse decomposition, which indicates its university. It also has certain antinoise ability for different types of noise.