记忆随机平均背景差模型在目标检测中的应用

Application of memory random shift average background difference model in moving object detection

为解决经典背景差方法在运动目标检测过程中由于目标短时间滞留导致漏检问题,提出一种记忆随机平均背景差模型的背景建模方法。在记忆随机平均背景差中,将视频中首帧划分为块,计算连续两帧之差作为初始背景,从当前帧往前随机选择一定帧的均值作为背景,通过当前帧减去背景即可得到运动目标。经在国际公开数据集(SBI)上进行大量实验,验证了设计的精准性和有效性。

Object detection is an important issue in computer vision. In order to solve the problem of missing detection caused by short delay of object in the process of moving object detection of the classical background difference method,a background modeling method of random drift average background difference model is proposed. The first frame in video is divided into blocks. Next,the calculated difference between two continuous frames is taken as the initial background. Then,the mean value of a certain frame in front of the current frame is randomly selected. The moving target can be obtained by subtracting the background from the current frame. A large number of experiments have been carried out on the international public data set to verify the accuracy and effectiveness of the design.