基于运动目标自适应检测的改进ViBe算法

摘要：

传统视觉背景提取（ViBe）算法检测结果存在Ghost区域， 且受环境变化影响， 在提取前景时容易产生误检或漏检。 针对这些问题， 提出了一种基于运动目标自适应检测的改进ViBe算法。 首先在背景模型初始化过程中， 通过对均值背景建模设置调节参数方式获取真实背景， 利用该背景初始化ViBe背景模型； 其次在前景检测过程中， 根据场景变化引入自适应半径阈值对前景进行自适应检测； 最后对检测结果中存在的空洞进行数学形态学闭运算填充。 实验结果表明， 改进算法能够有效抑制Ghost区域， 并在环境变化的情况下较完整地检测前景目标， 与传统ViBe算法相比， 检测的精确率提高了10%以上， 误检率和漏检率分别降低了20%和7%， 且改进算法满足实时性要求。

Abstract： There exists a Ghost region in the detection result of the traditional visual background extraction (ViBe) algorithm, and the foreground extraction is prone to false detection or missed detection due to environmental changes. Therefore, an improved ViBe algorithm based on adaptive detection of moving targets was proposed. Firstly, in the background model initialization process, the real background could be obtained by setting adjusting parameters in mean background modeling, and the ViBe background model was initialized by using the background. Secondly, in the foreground detection process, an adaptive radius threshold was introduced according to the scene change to adaptively detect the foreground. Finally, mathematical morphological close operation was used to fill the holes in the detection results. The experimental results show that the improved method can effectively suppress the Ghost region and detect the foreground target more completely under the condition of environmental changes. Compared with the traditional ViBe algorithm, the detection accuracy is improved by more than 10%, the false detection rate and the missed detection rate are reduced by 20% and 7% respectively. In addition, the improved method satisfies the real-time requirements.