[en] Estimating the stationary background of a video sequence is useful in many applications like surveillance, segmentation, compression, inpainting, privacy protection, and computational photography. To perform this task, we introduce the LaBGen-P method based on the principles of LaBGen and the conclusions drawn in the corresponding paper. It combines a pixel-wise median filter and a pixel selection mechanism based on a motion detection performed by the frame difference algorithm. By working with pixels instead of patches, as originally done in LaBGen, it avoids some discontinuities between different spatial areas and generates better visual results. In this paper, we describe the LaBGen-P method, study its performance on the sequences of the SBMnet dataset, and compare it to that of LaBGen and other methods on the same dataset. Both algorithms emerged as the best ones during the IEEE Scene Background Modeling Contest (SBMC) organized in 2016. However, as there is not yet a good understanding of the recommended metrics, and due to the small amount of video sequences provided with the corresponding ground truth, we have performed a subjective evaluation. More precisely, 35 human experts were asked to compare background images estimated by LaBGen-P and LaBGen, and select the best one. From these experiments, it turns out that the results of LaBGen-P are preferred for about two thirds of the video sequences. Note that we provide an open-source C++ implementation at http://www.telecom.ulg.ac.be/labgen.