为了更好地进行运动目标检测，提出了一种基于概率统计的自适应背景模型的运动目标检测方法。该方法能够自适应选择背景和前景阈值，且不需要进行训练，而且在不同的场景下能够自适应进行选择。在此基础上，针对盲目更新和选择更新不足，还采用了像素聚类统计和概率相结合的背景更新模型，因为采用基于像素统计的更新机制能够适应场景中背景的局部改变(移入/移出物体)，而采用概率更新则能够降低前景污染背景的程度。实验证明，该方法能够得到可靠的背景，改善了运动检测效果。

This paper proposed an adaptive background model for motion detection based on statistic information of probabilities. The approach can select thresholds of foreground and background adaptively and adapt to different scenes without training samples and human concerned. By using statistic information of historical pixels to update background can deal with moved/inserted objects in background. Meanwhile, a background model updated according to statistical characteristic is also provided in this paper. It gets ideal background and good detection results. Experimental results demonstrate the proposed algorithms can get relative good background and improve detection results for different scenes.