改进型ViBe算法在运动目标检测中的应用

作为计算机视觉领域的热门方向之一，运动目标检测具有很高的理论研究价值和很广的实际应用空间。传统视觉背景提取器(Visual Background Extractor, ViBe)目标检测算法实时性高且内存消耗低，但存在受光照影响大、不能有效抑制拖影区域、无法消除阴影以及检测图像内部空洞等问题。鉴于以上不足，提出3点针对性改进策略：(1)优化算法核心参数。筛选最优值来替换以往经验值，从而提高算法性能，增强算法适应性。(2)引入光强检测算子。阈值半径随光强变化自适应，避免因光照变化而出现拖影区域。(3)增加阴影检测模型。利用感兴趣区域(Region of Interest, ROI)像素分布确定阴影位置，结合运动目标自身特性分割出目标区与阴影区。仿真实验结果证明：改进型ViBe算法不仅能够完整地检测、抓取运动目标，而且还可以有效地抑制拖影区域并消除目标阴影。

Application of Improved ViBe Algorithm in Moving Target Detection

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.