Chen Min, Botchen Ralf P, Hashim Rudy R, Weiskopf Daniel, Ertl Thomas, Thornton Ian M
Computer Science, Swansea University.
IEEE Trans Vis Comput Graph. 2006 Sep-Oct;12(5):1093-100. doi: 10.1109/TVCG.2006.194.
Video visualization is a computation process that extracts meaningful information from original video data sets and conveys the extracted information to users in appropriate visual representations. This paper presents a broad treatment of the subject, following a typical research pipeline involving concept formulation, system development, a path-finding user study, and a field trial with real application data. In particular, we have conducted a fundamental study on the visualization of motion events in videos. We have, for the first time, deployed flow visualization techniques in video visualization. We have compared the effectiveness of different abstract visual representations of videos. We have conducted a user study to examine whether users are able to learn to recognize visual signatures of motions, and to assist in the evaluation of different visualization techniques. We have applied our understanding and the developed techniques to a set of application video clips. Our study has demonstrated that video visualization is both technically feasible and cost-effective. It has provided the first set of evidence confirming that ordinary users can be accustomed to the visual features depicted in video visualizations, and can learn to recognize visual signatures of a variety of motion events.
视频可视化是一个计算过程，它从原始视频数据集中提取有意义的信息，并以适当的视觉表示形式将提取的信息传达给用户。本文按照一个典型的研究流程对该主题进行了广泛探讨，该流程包括概念形成、系统开发、路径寻找用户研究以及使用实际应用数据进行的现场试验。特别是，我们对视频中运动事件的可视化进行了基础研究。我们首次在视频可视化中部署了流可视化技术。我们比较了视频不同抽象视觉表示的有效性。我们进行了一项用户研究，以检验用户是否能够学会识别运动的视觉特征，并协助评估不同的可视化技术。我们将我们的理解和开发的技术应用于一组应用视频片段。我们的研究表明，视频可视化在技术上是可行的，并且具有成本效益。它提供了第一组证据，证实普通用户可以习惯视频可视化中描绘的视觉特征，并能够学会识别各种运动事件的视觉特征。
