The accuracy of moving target detection will be significantly impacted by environmental conditions including rain, snow, and continually changing lake surfaces. Therefore, the main task of moving target identification in complicated scenarios is precisely identifying foreground targets from dynamic backgrounds. A moving target identification approach combining the visual background extractor (Vibe) algorithm with the improved local binary mode (LBP) feature operator is suggested in order to address the issue that the current Vibe algorithm has poor detection performance under complicated backdrops and is easily affected by changes in illumination. First, the LBP value image of each frame is calculated and saved, and the adjacent frame compensation strategy is used to stabilize the image to reduce the influence of illumination on the gray value. The background model is then created using the Vibe algorithm, and the foreground target is then obtained by performing morphological operations after replacing the gray value with an improved LBP value for foreground detection. The experimental results show that, compared with other traditional algorithms, the proposed method has a good suppression effect on the dynamic background. The recall rate has increased by an average of 25.6%, the accuracy rate has been increased by an average of 12.5%, and the false detection rate has been reduced by an average of 22.6% when compared to the original Vibe algorithm.