针对低对比度、低信噪比等复杂环境下运动目标检测失检率较高的问题，提出了基于稳定性主成分寻踪的运动目标偏振成像检测方法。首先将预处理后的连续帧偏振图像组合成一个矩阵，依据帧间图像信息相关性，建立了稳定性主成分寻踪数学模型，将该矩阵分解成低秩、稀疏，噪声三部分，其中稀疏矩阵包含了帧间目标信息;再以低秩矩阵核范数与稀疏矩阵1范数的和为目标函数，利用增广拉格朗日乘子法求得目标函数值最小时的稀疏矩阵;最后采用马尔科夫随机场滤除稀疏矩阵中的噪声。实验结果表明，该方法对复杂环境有很好的适应能力，且检测准确率优于其他算法。

Aiming at solving detection problem of moving objects in complex scenes that have the features of low contrast and low Signal-to-noise ratio, a polarization imaging detection method based on stable principal component pursuit was proposed. Firstly, a matrix was composed by pre-processed polarization image sequences. Based on correlation of image sequences, stable principal component pursuit mathematical model was build. The matrix could be decomposed to a low-rank matrix, a noise matrix, and a sparse matrix containing object information. Secondly, sparse matrix was achieved using augmented Lagrange multiplier method when the value of object function was sum of nuclear norm of the low-rank matrix and 1 norm of the sparse matrix was the smallest. Finally, Markov random field was used to remove noise in sparse matrix. The experimental results show that the adaptability to complex scenes and the accuracy rate of this method is better than other detection methods.