The accuracy of moving object detection based on traditional Gaussian mixture model is not high. In order to solve this problem, an improved method for moving object detection was proposed, which could improve the accuracy of moving object detection by Gaussian mixture model with spatial local correlation. Firstly, the Gaussian mixture model was built for each pixel in image, and the algorithm of the updating the variance coefficients based on the number of model matching was used to solve the problem that the variance converged slowly in traditional method. Then, the energy function of the Markov random field was redefined and combined with the spatial local correlation, and an adaptive threshold was obtained for moving object detection. By using the public available test data set from IBM Research, the experiments were carried out. Test results illustrate that the proposed method can adapt to the dynamic scenes much better, and obtain more accurate results of moving object detection.