Abstract: In order to detect moving vehicles correctly under the conditions that scene intensity changes rapidly，this paper proposes an online background reconstruction method based on image blocks and K-means clustering.First，the input image is divided into blocks overlapped with each other.Each block uses adaptive match threshold to form several clusters.Then，gain compensation is applied to images of the clusters which are candidates for background to reduce the intensity difference between adjacent blocks.In this process，prior gains are used to make the background intensity close to the input.Finally，current background image is reconstructed with linear blending method to eliminate the edges between blocks.Experimental results show that the method proposed has a good effect in different conditions.

摘要： 为了在场景亮度快速变化的情况下能正确地检测运动车辆，提出了一种基于分块建模和K-means聚类的在线背景重建算法。首先将图像划分为互相重叠的子块，使用自适应匹配阈值对每个子块进行聚类。然后对各子块中选为背景那一类的图像进行增益补偿，减少相邻子块间亮度差异，并且通过设置先验增益使背景亮度符合当前输入图像。最后，使用线性混合消除子块间边缘，得到当前帧的背景。实验表明，该算法在不同的环境和亮度变化下均能取得较好的效果。