大型室内活动中获取的室内人员轨迹数据具有时空复杂性高、高维且不规则等特点,给可视分析带来了一定挑战。针对该问题,面向室内人员的时空模式、人群移动模式、异常行为模式等设计了一种基于兴趣区(AOI,area of interest)划分的室内轨...
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大型室内活动中获取的室内人员轨迹数据具有时空复杂性高、高维且不规则等特点,给可视分析带来了一定挑战。针对该问题,面向室内人员的时空模式、人群移动模式、异常行为模式等设计了一种基于兴趣区(AOI,area of interest)划分的室内轨迹可视分析方法 ,用户可自定义兴趣区并以此为单位进行室内轨迹分析,从而确定其时空模式、移动模式或异常行为。最后,使用China Vis2019挑战赛的数据验证了所提方法的有效性,达到了通过探索式分析室内人员轨迹获取有价值信息的目的。
Nowadays, devices attached with position detecting techniques are used on many places to track moving of objects. The collected time and position records, which constructed moving trajectories of objects, are in huge ...
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Nowadays, devices attached with position detecting techniques are used on many places to track moving of objects. The collected time and position records, which constructed moving trajectories of objects, are in huge amount. Among the object trajectories, interesting moving behaviors are hidden and worth to be revealed through some processing. In this dissertation, we focus on analyzing object moving behaviors through trajectory data profiling and *** an area where a set of objects moving around, there are some typical moving behaviors of objects at different regions in respect to the geographical nature or other spatiotemporal conditions. Not only paths that objects moving along, we also want to know how different groups of objects move with various speeds. Therefore, given a set of collected trajectories spreading in a bounded area, we are interested in discovering typical moving styles in different regions of all monitored moving objects. These regional typical moving styles are regarded as profile of the monitored moving objects, which may help reflect the geographical information of observed area and the moving behaviors of observed moving objects. However, an object can move with various speeds and arbitrarily changing directions. The changes cause difficulty in analyzing behaviors among object trajectories. Thus, we present DivCluST, an approach to finding regional typical moving styles by dividing and clustering trajectories in consideration of both spatial and temporal constraints. Different from existing works that considered only spatial properties or just some interesting regions of trajectories, DivCluST focuses more on finding typical regional spatiotemporal behaviors over a large area. It takes both spatial and temporal information into account when designing the criteria for trajectory dividing and the distance measurement for adaptive k-means clustering. Extensive experiments on three types of real data sets with specially designed visual
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