基于图模型与增量学习的网络化智能视频监控研究

项目名称： 基于图模型与增量学习的网络化智能视频监控研究

项目编号： No.61271390

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 无线电电子学、电信技术

项目作者： 王贵锦

作者单位： 清华大学

项目金额： 76万元

中文摘要： 如今世界几乎已是摄像头监视的世界, 海量摄像头监控网络将是社会重要组成部分。但人眼实时监看所有画面既不经济也不现实，如何分析这些海量网络化视频是监控网络普及的关键。为求实用，已有研究注重单摄像头内事件分析或较少摄像头间画面关联。受摄像视野所限、光照变化、目标遮挡等因素，对整个监控网覆盖区事件分析性能检测率受限、虚警率高。本项目拟对物理上互连成网的摄像监控系统用图结构进行建模，节点属性含区域内提取的事件局部特征, 如视野内有无目标、其拥挤度、运动轨迹、进出时间、光流场有序度等；节点间的边反映区域局部观测信息间关联，如目标进出的时间关联、轨迹的空间位置关联，目标间表观相似性关联、光流有序度关联等。通过代价函数最小化，用增量学习动态适应监控场景变化，"训练"图模型中节点状态和边连接及其对事件检测的权重。不以特定应用为目标，我们探索图模型及增量学习等较一般化的视频监控网络基础理论和方法。

中文关键词： 视频分析；距离成像；目标对应；姿态识别；迁移学习

英文摘要： Wide-area and complex public scenes are now often monitored by numerous cameras, networked camera surveillance becomes one of the most important part of the society. However, it is not economical and nor practical to watch all these videos by man in traditional way. The inner key scientific problem is how to efficiently extract/mine useful information from the numerous surveillance videos. Many researches have been done to detect events in single cameras or associate object across a few cameras.However,in practical scene, the detection performance suffers when the objects move across camera views and experience different illumination conditions, occlusion, and etc. We propose Graph structure to model the physically inter-connected camera surveillance system:the node extract the partial event feature, like the status of object existance in local region, the crowded density, moving trajectory, timestamp of entrance and departure, the disorder degree of the optical field, and etc; the edge represents the corelationship of local observation information, like the correlation of entrance timestamp, the correlation of trajectory, correlation of object appearance, and etc.By optimizing the cost function, we present incremental learning to adapt to the changing environment, train the components of graph structuture (nod

英文关键词： video analysis；depth imaging；object corresponding；gesture recognition；transfer learning