一种基于自适应混合模型的背景减除法

A background subtraction method based on adaptive hybrid model

针对局部二进制相似度(LBSP)背景建模方法易受外界环境变化如动态背景、光照改变、相机抖动等干扰的问题,在融合像素纹理与亮度信息的基础上,建立一种自适应混合背景模型进行运动目标检测。首先,利用每个像素的多通道自适应局部二进制相似度(LBSP)信息和亮度信息建立混合背景模型。然后,根据当前像素与混合背景模型的比较结果对其进行分类,并采用随机更新机制更新背景模型。实验结果表明,本方法不仅在正常外界环境下取得了较好的检测结果,而且还可以有效地减少动态背景、光照变化等复杂外界环境条件造成的干扰,提高检测结果的准确性。

In view that the existing background modeling method based on local binary similarity pattern （LBSP） is very vulnerable to external environment changes, such as dynamic background,illumination changes, camera shaking and so on, based on fusing pixel texture information with intensity information,we propose an adaptive hybrid model for moving object detecting. First, we use the texture descriptor of each pixel, named multi-channel adaptive local binary similarity pattern （LBSP） information,combined with intensity information,to build a hybrid background model. We then classify the current pixels according to the comparison results between the current pixel and the corresponding hybrid background model,and update the background model with the random updating mechanism. Experimental results show that the proposed method cannot only achieve good results in an ideal outside environment, but also effectively reduce the interference caused by complicated external environment conditions such as dynamic background, illumination changing and camera shaking, thus achieving better detection results.