轨迹数据的稳健可视化

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ying Zhang, Karsten Klein, O. Deussen, Theodor Gutschlag, Sabine Storandt
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引用次数: 1

摘要

摘要运动轨迹的分析在许多应用领域发挥着核心作用,如交通管理、体育分析和集体行为研究,这些领域通常会收集大量复杂的轨迹数据集。虽然自动化分析方法可用于提取轨迹的特征,例如可能与重要事件相关联的几何结构、运动模式和位置的统计数据,但仍需要人工检查来解释结果、导出分析参数、比较轨迹和模式,并进一步解释影响轨迹形状及其潜在运动过程的影响因素。采集和分析流程中的每一步都可能引入伪影或改变轨迹特征,这可能会使人类的解释产生偏差或混淆自动分析。因此,可视化方法以及可视化本身需要考虑相应的因素,以便在不添加或删除重要轨迹特征或给分析员带来巨大压力的情况下进行合理的解释。在本文中,我们概述了鲁棒轨迹可视化任务中出现的挑战。然后,我们讨论了几种有助于改进可视化的方法。特别是,我们提出了简化轨迹集的实用算法,这些算法直接考虑了语义和不确定性信息。此外,我们描述了一种互补的方法,该方法允许将不确定性与轨迹一起可视化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust visualization of trajectory data
Abstract The analysis of movement trajectories plays a central role in many application areas, such as traffic management, sports analysis, and collective behavior research, where large and complex trajectory data sets are routinely collected these days. While automated analysis methods are available to extract characteristics of trajectories such as statistics on the geometry, movement patterns, and locations that might be associated with important events, human inspection is still required to interpret the results, derive parameters for the analysis, compare trajectories and patterns, and to further interpret the impact factors that influence trajectory shapes and their underlying movement processes. Every step in the acquisition and analysis pipeline might introduce artifacts or alterate trajectory features, which might bias the human interpretation or confound the automated analysis. Thus, visualization methods as well as the visualizations themselves need to take into account the corresponding factors in order to allow sound interpretation without adding or removing important trajectory features or putting a large strain on the analyst. In this paper, we provide an overview of the challenges arising in robust trajectory visualization tasks. We then discuss several methods that contribute to improved visualizations. In particular, we present practical algorithms for simplifying trajectory sets that take semantic and uncertainty information directly into account. Furthermore, we describe a complementary approach that allows to visualize the uncertainty along with the trajectories.
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来源期刊
IT-Information Technology
IT-Information Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.80
自引率
0.00%
发文量
29
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