Atmospheric turbulence can cause the image degraded with time-varying blur and geometric distortion. We resolve the object detection problem by proposing a three-step approach. According to the low-rank decomposition, the first step decomposes the turbulence sequence into two components:the low-rank stabilized background and the sparse errors. The sparse part in the result of first step includes turbulence distortion, noise and moving object. Then, the sparse matrix is processed with adaptive threshold to select the block-sparse mask and the holes within the mask are simultaneously filled. The low-rank matrix is processed with different Gaussian models to extract the foreground. Finally, a decision fusion module is introduced to exploit complementary information from two approaches to boost overall detection accuracy. The experimental results have shown the promising performances. Compared with traditional methods, the proposed approach can not only improve the detection rate, but also handle the interferences of strong turbulence.