一种自适应运动目标检测算法及其应用

Adaptive Moving Target Detection Algorithm and Its Application

针对ViBe算法在动态背景下存在鬼影消除时间长、算法适应性差、前景检测噪声多的问题,本文提出一种基于ViBe算法框架的改进算法.该算法采用鬼影检测法标记第1帧中的鬼影区域,并向位于鬼影区域的背景模型中强制引入背景样本,从而快速抑制鬼影;在像素分类过程中,引入自适应分类阈值,解决全局阈值易受动态噪声干扰的问题;在背景模型更新中,根据像素分类的匹配值来动态决定更新因子,提高算法适应场景变化的能力.定性与定量的对比实验结果表明,本文算法相较于ViBe算法能够有效地检测动态背景下的运动目标,应用于河流漂浮物检测场景中也有较好的效果.

Aiming at the problem that ViBe algorithm has long ghost elimination time,poor algorithm adaptability and high foreground detection noise in dynamic background,this paper proposes an improved algorithm based on ViBe algorithm framework.The algorithm uses the ghost detection method to mark the ghost region in the first frame,and forces the background sample into the background model in the ghost region to quickly suppress the ghost.In the pixel classification process,the adaptive classification threshold is introduced to solve the problem that the global threshold is susceptible by dynamic noise interference.In the background model update,the update factor is dynamically determined according to the matching number of the pixel classification to improve the algorithm's ability to adapt to scene changes.The comparison experimental results of qualitative and quantitative shows that the algorithm in this paper can effectively detect moving targets in dynamic background compared to the ViBe algorithm,and it also has a better effect in the detection of river floating objects.