运动目标三维轨迹可视化与关联分析方法

随着治安监控系统的普及,越来越多的监控摄像头被安装在各个交通道路和公共场所中,每天都产生大量的监控视频.如今,监控视频分析工作主要是采用人工观看的方式来排查异常,以这种方式来分析视频内容耗费大量的人力和时间.目前,关于视频分析方面的研究大多是针对目标个体的异常行为检测和追踪,缺乏针对对象之间的关联关系的分析,对视频中的一些对象和场景之间的关联关系等还没有较为有效的表示和分析方法.针对这一现状,提出一种基于运动目标三维轨迹的关联视频可视分析方法来辅助人工分析视频,首先对视频资料进行预处理,获取各个目标对象的运动轨迹信息,由于二维轨迹难以处理轨迹的自相交、循环运动和停留等现象,并且没有时间信息就难以对同一空间内多个对象轨迹进行的关联性分析,于是结合时间维度对轨迹进行三维化扩展.该方法支持草图交互方式来操作,在分析过程中进行添加草图注释来辅助分析.可结合场景和对象的时空关系对轨迹进行关联性计算,得出对象及场景之间的关联模型,通过对对象在各个场景出现状况的统计,结合人工预先设定的规则,可实现对异常行为报警,辅助用户决策.

Method of Three Dimensional Trajectory Visualization and Relational Analysis on Moving Objects

With the popularity of security surveillance systems, more and more surveillance cameras have been installed at various traffic roads and public places. A lot of surveillance videos are produced every day. Currently, surveillance video analysis is being done by manual monitoring, which is very inefficient. Most researches on video analysis focus on abnormal behavior detection and tracking of the target individual and lack of analysis of associations between objects/scenes, and there have no effectively representation and analytical methods for the association between objects and scenes. This paper presents a three-dimensional trajectory of moving objects associated video visual analysis to assist manual analysis of videos. First, video data preprocessed to get trajectory information of target object. Because two-dimensional trajectory is unable to deal with the recurring motion, self-intersecting motion and pause, and also lacks of time information, it is difficult be used to analyze correlation between multi-object trajectories in same space. Therefore, time dimension is added to generate three-dimensional trajectory to unite scenes and objects to calculate correlation between trajectories and build the association model. This approach supports sketch interaction so that users can add sketch annotation to assistant analyzing. By utilizing the absence information of objects in certain scene and some predefined rules, the presented approach can be used to alert the abnormal action and assistant user to make corresponding decisions.