动态背景下基于改进视觉背景提取的前景检测

由于视觉背景提取算法（ViBe）对存在动态背景的户外视频的前景检测结果依然不够精确,故提出了一种改进的ViBe算法。文中描述了经典ViBe算法及其特点;介绍了改进的ViBe算法针对动态背景的改进措施。该算法采用多帧连续图像初始化背景模型,降低了单帧图像初始化所产生的“鬼影"对前景检测精度的影响;在匹配过程中,引入自适应的匹配阈值,克服了单个的全局阈值对动态背景适应能力差的问题;最后,在更新过程引入空间一致性判断与模糊准则来减少算法的误检,提高了算法的鲁棒性。实验结果表明,该算法可以有效地检测动态背景下的运动目标,检测准确率比经典ViBe算法提高了20%以上。

Foreground detection based on modified ViBe in dynamic background

As Visual Background Extractor(ViBe) can not implement foreground detection precisely for a particular scene with dynamic backgrounds, This paper proposes a modified ViBe algorithm. It describes the original ViBe algorithm and its characteristics and discusses several modification schemes for the original ViBe in dynamic background scenes. Firstly, model initialization is conducted with several continuous frames instead of one single frame to handle ghosts. Then, self-adaptive threshold is adopted in the process of model matching so that background models is better suitable for the dynamic background. Finally, a spatial coherence estimation and a fuzzy rule in model maintenance are proposed to reduce false detections and to improve the robustness of the algorithm. Experiments demonstrate that the algorithm proposed detects effectively the movement targets in dynamic background scenes and its precision is improved by 20 percent as compared with that of the original ViBe algorithm.