在含有动态干扰因素的复杂背景下提取前景目标时,现有的视觉背景前景目标提取算法容易出现鬼影、误检等问题,因此提出了一种改进的基于视觉背景的前景目标提取算法。首先,根据像素点的时间序列以及位置特征,计算像素点的匹配概率、匹配程度以及亮度信息。其次,实时更新与当前复杂背景吻合的背景模型,同时对背景模型进行初始化。最后,对CDnet 2014数据集中各类复杂背景下的视频进行测试,并与经典的高斯混合模型、视觉背景提取(ViBe)算法、改进的ViBe算法进行对比。实验结果表明,本算法在各类复杂背景下能高效去除鬼影的影响,且提取结果精度较高,极大降低了提取结果的错分率和漏检率,提高了复杂背景下算法的高效性与鲁棒性。

When extracting foreground targets in the complex background with dynamic interference factors, the existing algorithms of extracting foreground targets in visual background are prone to ghost image and false detection, so an improved algorithm based on visual background is proposed in this paper. First, according to the time series and position characteristics of pixels, the matching probability, matching degree, and brightness information of the pixels are calculated. Second, background model matching the current complex background is updated in real time, and the background model is initialized. Finally, the video in various complex backgrounds in the CDnet 2014 dataset is tested, and compared with the classic Gaussian mixture model, visual background extraction (ViBe) algorithm, and improved ViBe algorithm. Experimental results show that the algorithm can efficiently remove the effects of ghosts in various complex backgrounds, had a high extraction precision, which greatly reduces the misclassification rate and missed detection rate of the extraction results, and improves the efficiency and robustness of the algorithm in complex background.