基于超像素信息反馈的视觉背景提取算法

Visual Background Extraction Algorithm Based on Superpixel Information Feedback

针对经典视觉背景提取算法长时间存在鬼影、动态背景导致的高频噪声以及背景模型误更新等问题,提出一种改进的视觉背景提取算法。该算法将原始图像分割为若干个超像素区域,在超像素分割区域,对视觉背景提取算法检测结果进行像素点再分类,在目标检测的初始阶段实现鬼影信息的准确检测,并更新鬼影区域像素点的背景模型,从根本上解决了全局范围内鬼影检测的难题。根据运动目标的超像素对前景目标内的空洞进行快速纠正,实现前景目标的小范围填补,同时完成对背景超像素内高频噪声的检测和滤波,并增强检测结果的稳健性。利用数据集进行的测试实验结果表明,与传统算法相比较,该算法的精确率和识别率等指标均显著提高。

To solve the problems about the ghost,high frequency noises from dynamic background and background model update error,an improved visual background extraction algorithm is proposed.The original image is accurately segmented into several regions by employing the superpixel model.The superpixels of true moving object from visual background extraction results are reclassified.And the ghost region is accurately identified,which can immediately detect and feedback ghost information to refresh its background model.Thus,the key problem about ghost region detection in global scale is resolved.According to the superpixel segmentation results,the small noise objects are discarded and the holes filling strategies are added to enhance robustness of the proposed algorithm.Experimental results show that the precision and recognition rate are remarkably improved by employing standard datasets.