As one of the popular directions in the field of computer vision, moving target detection has high theoretical research value and wide practical application space. Traditional visual background extractor (ViBe) target detection algorithm has high real-time performance and low memory consumption. However, this algorithm has many problems, such as obvious illumination change, inability to effectively suppress ghost area, inability to eliminate shadows, and inability to detect holes in the image. In view of the above deficiencies, three targeted improvement strategies are proposed: (1) Optimize the core parameters of the algorithm. Filter the optimal value to replace the previous experience value, so as to improve the performance and adaptability of the algorithm. (2) Introduce the light intensity detection operator. The image brightness is numerical and the threshold radius is adaptive to avoid ghost area due to light changes. (3) Add shadow detection model. The pixel distribution in the region of interest (ROI) determines the shadow position, and the target area and shadow area are separated according to the characteristics of the moving target. Simulation results show that the improved ViBe algorithm can not only detect and capture moving targets completely, but also effectively suppress ghost areas and eliminate target shadows.