基于边缘特征的背景建模和去抖动方法

 针对户外监控系统中存在的背景复杂变化及摄像机抖动等问题，提出了一种利用背景边缘信息进行背景建模及去除摄像机抖动的方法。首先，对一段视频序列进行边缘检测，提取出可靠的背景边缘；然后，对处在背景边缘附近的复杂区域建立高斯混合模型，而对其他相对简单的区域建立时间平均模型，兼顾了检测精度和计算代价；再利用可靠的背景边缘信息消除因摄像机的抖动而出现的虚假目标。实验结果表明，该方法在检测速度上比单独采用高斯混合模型提高了50%，在摄像机抖动时能很好地降低虚警率，可用于复杂场景的运动目标检测。

A Background Model and the Method of Removing Camera Shake Effects Based on Edge Features

Outdoor surveillance systems would be subjected to illumination changes and camera shakes to cause changes in the background. This paper proposes a background model and a method of removing camera shake effects based on edge features. The edge features of the background are handy to have a representation of the scene background invariant to illumination changes. Firstly, reliable background edges are extracted by edge detection algorithms from a video sequence. Secondly, the Gaussian Mixture Model (GMM) is employed in the regions near the background edges, while the Temporal Average Model (TAM) is used for other regions. Both the detection accuracy and speed are considered. Finally, to eliminate the false target caused by camera shakes, a method using the reliable background edges is proposed. A background update mechanism is also used. The experiment results show that the detection speed is increased by 50% than the GMM method. The detection accuracy is better than the TAM method. The advantages of the GMM and TAM methods are well combined in the new method. When the camera is shaking, the false alarm rate is reduced by using background edge features. Good detection results are obtained as compared to the GMM and TAM methods. The method can be used for moving object detection in complex scenes.