基于并行框架的鲁棒自适应前景检测算法

视频监控数据TB级的增长，从海量视频数据中高效准确的分离出视频监控场景中的运动物体，是计算机视觉领域的研究重点和挑战。提出了基于云平台的视频数据处理的并行计算框架及一种改进的基于混合高斯模型(GMM)的自适应前景提取算法，通过对混合高斯分布的自适应学习和在线 EM(期望最大化)算法获得最优参数组合，并将改进算法融合到视频处理并行计算框架。实验结果表明，该方法不但能大大提高视频处理的效率，并对复杂环境下准确提取前景目标也有良好的鲁棒性。

Robust Adaptive Foreground Detection Algorithm Based on Parallel Framework

Video surveillance data is increasing quickly, it's a challenge to separate out moving objects from a massive video data in the field of computer vision. The article designs and implements a Cloud-based distributed video processing framework, and proposes an improved adaptive foreground extraction algorithm based on gaussian mixture model(GMM). The method obtains the optimal parameters by adaptive learning gaussian distribution and online EM(Expectation Maximization) algorithm, and it fuses the improved algorithm to distributed video processing framework. The experiment shows that the method can not only greatly improve the efficient of video processing but also accurate extract foreground targets under complex environment , and it has good robustness.