Pixel-based MOG background modeling is not a good method to deal with moving target detection in dynamic background. It is hard to model the dynamic background in this method due to the complex movement of background pixels. Thus, huge sums of false positives would occur. This paper aims to suppress the false positives in space domain and time domain. In space domain, class-prior probability and class-conditional-probability of a pixel were calculated from MRF model and Mixture of Gaussians (MOG) model. By combining the class-prior probability and class-conditional-probability, foreground segmentation was completed to suppress a majority of false positives of small size. In time domain, three target characteristics were observed: motion constancy, motion saliency and area stabilization.The result of experiments show that the false positives on dynamic background could be suppressed to a great extent,meanwhile, good detecting accuracy is ensured.