基于时空背景模型的自适应运动目标检测方法

Adaptive moving object detection method based on spatial-temporal background model

现有的视觉背景提取方法(Vi Be)在背景建模时只利用了像素的空间信息,而忽略时间信息,降低了检测的准确性,且检测半径和背景更新的随机子采样因子都为固定常数,在动态背景干扰、相机抖动等情况下,检测效果不理想。针对这些问题,提出一种时空背景模型的自适应运动目标检测方法。首先,在Vi Be方法中加入时间信息建立时空背景模型;然后,在检测和更新过程中,提出背景模型中样本的标准差能反映背景的复杂度,通过计算样本的标准差来自适应地改变检测半径和背景更新的随机子采样因子适应背景的变化。实验结果表明,改进的方法不仅能够在静态背景和光照均匀的情况下有效地检测出前景像素,而且对存在光线变化较大、相机抖动、动态背景干扰等情况也有一定的抑制作用,提高了检测的准确性。

The available Visual Background extractor （ViBe） only uses the spatial information of pixels to build background model ignoring the time information, as a result to make the accuracy of detection decrease. In addition, the detection radius and random sampling factor of updating background model are fixed parameters, the effect of detection is not ideal on the circumstances of dynamic background interference and camera shake. In order to solve these problems, an adaptive moving target detection method based on spatial-temporal background model was proposed. Firstly, the time information was added to ViBe to set up spatial-temporal background model. And then the complexity of the background was reflected by the standard deviation of the samples in the background model. So the standard deviation was able to change the detection radius and random sampling factor of updating background model to adapt to the change of background. The experimental results indicate that the proposed method can not only effectively detect the foreground with static background and uniformity of light, but also have certain inhibitory effects in the cases of the light changing greatly, camera shaking, and the dynamic background interference, and so on. It is capable of improving the precision of detection.