基于改进视觉背景提取算法的运动目标检测

针对经典视觉背景提取算法（visual background extraction algorithm, ViBe）因初始帧存在运动目标易产生鬼影以及对扰动背景适应性差的问题，提出一种改进ViBe算法。利用改进三帧差分法和最小外接矩形定位初始帧运动目标，并通过局部初始化的方法进行鬼影抑制；在背景模型初始化阶段，定义灰度相似函数从时域和空域信息中中等比例选取像素点建立背景模型,增强背景模型的鲁棒性；在前景检测检测阶段，通过平均差法衡量样本集合的离散度，构建自适应分割阈值代替原有的固定分割阈值以适应背景扰动。实验表明，改进算法可以有效抑制鬼影产生并且提高算法在扰动背景下的适应性和检测准确度。

Moving Object Detection Based on Improved Visual Background Extraction Algorithm

Aiming at the problem that the classical visual background extraction algorithm (ViBe) is prone to ghost images due to the presence of moving objects in the initial frame and poor adaptability to dynamic backgrounds, an improved ViBe algorithm is proposed. Using the improved three-frame difference method and the minimum circumscribed rectangle. The initial frame moving target is located, and ghosting is suppressed by the method of local initialization; In the initial stage of the background model, a grayscale similarity function is defined to select pixels in a moderate proportion from the temporal and spatial information to establish a background model to enhance the robustness of the background model; In the foreground detection stage, the average difference method is used to measure the dispersion of the sample set, and an adaptive segmentation threshold is constructed to replace the original fixed segmentation threshold to adapt to the background disturbance. Experiments show that the improved algorithm can effectively suppress ghosting and improve the performance of the algorithm in disturbance Adaptability and detection accuracy in context.