In order to improve the recognition rate of target detection, a background modeling algorithm based on improved Student-t distribution parameter estimation is proposed in this paper, which is suitable for all kinds of motion video target detection such as sports video application field. Firstly, the background modeling method based on the traditional finite model is analyzed. Then, the Student-t distribution is used to construct the background model parameter estimation method. The method is used to complete the parameter estimation by improving the maximum expectation algorithm, and the parameter space is segmented. The simulation results show that the segmentation effect of the proposed algorithm is better as compared with the Gaussian background modeling, K-means and fuzzy C-means clustering background modeling, and the motion target detection accuracy is higher and the training speed is faster. The results verify that the validity and advancement of the proposed algorithm.