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本文重点研究了如何利用稀疏表示理论来实现运动目标检测的方法,该方法首先针对非平稳的背景环境,构建和更新稀疏表示字典,实现对场景背景的有效表示.在进行运动目标检测时,利用l1范数计算当前帧图像在所构建的字典中的稀疏表示系数.并根据每个图像块的稀疏表示系数对图像进行重建,然后对重建误差进行阈值化,实现从背景中分割出运动目标.This paper focuses on how to use the sparse representation theory to realize the detection of moving target. The method first costructs and updates the sparse representation dictionary aiming at a Non-stationary background environment, so as to realize the effective representation.of the geometrical characteristics of the images. When making the moving target detection, the norm of l1 is used to calculate the sparse representation coefficients of the current frame image in the constructed dictionary. And the image is reconstructed based on the sparse representation coefficients of each image block, Then thresholding the reconstruction error to segment out the moving targets from the background.