融合子块梯度与线性预测的单高斯背景建模

Single Gaussian model for background using block-based gradient and linear prediction

针对单高斯背景模型不能适应非平稳场景且对初期保持静止后期运动的物体造成"鬼影"现象的问题,提出了融合子块梯度与线性预测的单高斯背景建模方法。首先,对每个像素点进行单高斯背景建模,并实现像素级的自适应更新,运用子块梯度算法将梯度在阈值内的子块作为背景以消除"鬼影";然后,将子块梯度法获得的前景与单高斯模型确定的前景做与运算,提高在非平稳场景下对背景的判断能力;最后,运用线性预测方法处理获得的前景点,将面积小于阈值的连通区域还原为背景。采用CDNET 2012 Dataset和Wallflower Dataset进行仿真实验:当场景变化幅度较大时,所提算法与混合高斯模型(GMM)相比,虽然检测率稍有下降,但检测精度提高了40%;在其他场景中检测率虽只提高约10%,检测精度却能提高25%以上。实验结果表明,融合子块梯度与线性预测的单高斯背景建模能够适应非平稳场景并消除"鬼影"现象,获得的背景比混合高斯模型更精确,提取的前景细节更丰富。

In order to solve the problem that the Single Gaussian Model（ SGM） for background could not adapt to nonstationary scenes and the ＂ ghost＂ phenomenon due to sudden moving of a motionless object. An SGM for background using block-based gradient and linear prediction was put forward. Firstly,SGM was implemented on the pixel level and updated adaptively according to the changes of the pixels＇ values,at the same time the frame was processed by the block-based gradient algorithm,obtaining the background by judging whether the gradient of sub-block was within the threshold value and eliminating ＂ ghost＂; and then foreground from the block-based gradient algorithm and that from the SGM were made ＂ AND＂ operation,improving the judgment of the background in non-stationary scenes; lastly the linear prediction was employed to process the foreground acquired from the previous operation,resetting the connected regions whose area was less than the threshold value as the background. Simulation experiments were conducted on the CDNET 2012 dataset and Wallflower dataset. In the scenes which varied by a large margin,the accuracy of the proposed method was 40% higher than that of the Gaussian Mixture Model（ GMM） in spite of the fact that the detection rate of the proposed method was lower than that of GMM; but in other scenes,the rate of detection was 10% higher and the accuracy was 25% higher. The simulation results show that the proposed method is able to accommodate to the non-stationary scenes and achieve the goal of wiping the ＂ ghost＂ off,as well as obtain a better result of the background and more detailed foreground than GMM.