基于随机背景建模的目标检测算法

运动目标检测是智能视频监控系统中的重要步骤和前提。提出了一种基于随机背景建模的非参数化建模算法，对场景中运动目标进行快速提取跟踪。在初始化阶段，从当前像素的邻域中随机抽取样本值作为背景模型；在模型更新阶段，引入了随机更新策略和背景传播机制，能够较好地抑制环境噪声；在后处理阶段，给出了一种基于积分图的前景滤波优化方法，进一步滤除噪声和填充前景空洞。实验结果表明，在复杂场景条件下，算法的目标检测性能明显优于其他几种同类算法，能够较好地抑制噪声干扰，具有较高的检测正确率。对于360288像素的测试视频，算法的计算速度高达120 f/s，完全可以满足实时应用。

Object detection based on randomized background modeling algorithm

Moving object detection is a key step in the system of intelligent visual surveillance. A randomized background nonparametric modeling algorithm was proposed for object fast detection application. In the initialization stage, a set of values were randomly taken in the neighborhood of the current pixel. In the update stage, a reasonable randomized updating and background samples propagation mechanism were introduced, which could restrain the scene noise effectively. In the post-processing stage, a foreground optimized filter based on the integral image was designed to further remove the noises and fill the holes of the objects. Experimental results show that the performance of the proposed algorithm is significantly superior to other similar algorithms. It can further restrain noise and has high detection precision. In addition, for 320240 pixel format video stream, the computing of the proposed detection algorith can reach up to 120 fps, which can fully meet the requirement of the real-time application system.