一种基于改进视觉背景提取算法的前景检测

针对视觉背景提取ViBe算法在前景检测中存在的鬼影现象且长时间难以消除的缺点，提出一种改进的视觉背景提取算法。首先，在视频前n帧序列的帧差法中，引入大津（OTSU）算法求自适应阈值，以分割出更为准确的前景区域；其次，利用去除前景区域的前n帧图像合成一张尽量少的包含前景区域的样本图像；最后利用扩展的邻域范围在合成的样本图像中对模型初始化，并把扩大的范围用在ViBe背景模型更新阶段。该算法与各种经典算法在大量视频库中进行了对比实验，仿真结果表明，改进的ViBe算法能快速消除鬼影对前景检测的影响，前景检测更为准确。

An improved visual background extraction algorithm for foreground detection

Visual background extraction algorithm (ViBe) for foreground detection has the disadvantage that there is ghost and it is difficult to eliminate it for a long time, so an improved visual background extraction algorithm is proposed. Firstly, being introduced in the first sequence with n video frames, the OTSU algorithm can calculate an adaptive threshold for the frame difference method so as to get a more accurate foreground region. Secondly, a sample image is synthesized by using the first n frame images without foreground regions. Finally, the model is initialized with the extended neighborhood range in the synthesized sample images, and the ViBe background model can be updated by the extended domain. The proposed algorithm is compared with various classical algorithms in a large number of video databases. Simulation results show that the improved ViBe algorithm can quickly eliminate the effect of ghost on foreground detection, so the foreground detection is more accurate.