改进视觉背景提取模型的前景目标检测算法

Foreground Object Detection Algorithm Based on Improved Visual Background Extraction Model

针对经典视觉背景提取算法(ViBe)在动态背景场景下检测精度不高,以及长时间存在鬼影的问题,提出一种改进的视觉背景提取算法.该方法在背景模型初始化阶段考虑到像素点之间的颜色相似性以及空间距离,选取像素点邻域内的同质像素点对背景模型进行初始化;根据场景动态程度自适应调整每个像素点的阈值以及背景模型更新的速率,改善了在动态背景场景下的检测精度;根据光流判断像素点是否存在运动来把真实前景目标和鬼影区分开来并及时对背景模型进行修正,从而尽快消除鬼影现象.使用changedection测试集进行测试,改进后的ViBe算法在能提取到较完整前景目标的同时,检测准确率相比原始ViBe算法也有所提高.

We propose an improved background extraction algorithm to tackle the problem of low detection accuracy and long ghosting in the classical background extraction algorithm(Vi Be)under dynamic background scenes.This method considers the pixel color similarity between spaces and distances in the background model initialization and selects homogeneous pixel dots in the neighborhood to initialize the background model.According to the scene dynamic degree rate adaptive adjustment of each pixel threshold and the background model update,the detection accuracy is improved in dynamic background scenarios.According to the optical flow,movement in the pixel points and the existence of motion target and ghost real prospects can be distinguished,and the background model can be timely modified,thereby eliminating ghosting as soon as possible.Using the change detection test,the improved Vi Be algorithm not only can extrat more complete foreground targets,but also can improre the detection accurary compared with original Vi Be alogrithm.