【摘要】 针对传统Vibe+算法存在噪点和拖影分割错误率较高的问题,提出了一种改进的Vibe+运动目标分割算法(Vibe++)。首先,通过对视频帧采用传统Vibe+算法处理获取二值图像,基于区域生长算法对结果图中各连通域标记,依据边界面积块差异获取面积筛选阈值,将低于阈值的连通区域视为噪点并删除;然后,引入5种不同核函数优化传统MeanShift聚类算法,并与卷积神经网络(CNN)进行顺序组合;最后,采用组合模型对已消除噪点图像中的拖影区、非拖影区和拖影边缘区分类,计算拖影区中每个像素点的坐标,定位拖影区并快速删除,获取分割结果。所提算法用于公开数据集的实验结果表明,其可取得98%以上的分割准确率,具有较好的应用效果和较高的实用价值。更多还原

【Abstract】 To solve problems of noise points and high segmentation error for image shadow brought by traditional Vibe+ algorithm, a novel background segmentation method(Vibe++) based on the improved Vibe+ was proposed. Firstly, binarization image was acquired by using traditional Vibe+ algorithm from surveillance video. The connected regions were marked based on the region-growing domain marker method. The area threshold was obtained with difference characteristics of boundary area, the connected regions below threshold were treated as disturbing points. Secondly, five different kernel functions were introduced to improve the traditional MeanShift clustering algorithm. After improving, this algorithm was fused effectively with partitioned convolutional neural network. Finally, program of classification of trailing area, non-trailing area and trailing edge area in the resulting image was performed. Position coordinates of the trailing area were calculated and confirmed, and the trailing area was quickly deleted to obtain the final segmentation result. This segmentation accuracy was greatly improved by using the proposed method. The experimental results show that the proposed algorithm can achieve segmentation accuracy of more than 98% and has good application effect and high practical value.更多还原