Interactive data-driven discovery of temporal behavior models from events in media streams

Chreston A. Miller, Francis K. H. Quek
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引用次数: 7

Abstract

This paper investigates a technique for the discovery of temporal behavior models within multimedia event data. Advancements in both technology and the marketplace present us the opportunity for research in analysis of situated human behavior using video and other sensor data (media streams). By situated analysis, we mean the study of behavior in time as opposed to looking at behavior in the form of aggregated data divorced from how they occur in context. Human and social scientists seek to model behavior captured in media, and these data may be represented in a multi-dimensional event data space derived from media streams. The knowledge of these scientists (experts) is a valuable resource which can be leveraged to search this space. We propose a solution that incorporates the expert in an iteratively, interactive data-driven discovery process to evolve a desired behavior model. We test our solution's accuracy on a multimodal meeting corpus with a progressive three tiered approach.
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交互式数据驱动的从媒体流中的事件中发现时间行为模型
本文研究了一种在多媒体事件数据中发现时间行为模型的技术。技术和市场的进步为我们提供了利用视频和其他传感器数据(媒体流)研究分析人类行为的机会。通过情境分析,我们指的是对行为在时间上的研究,而不是以汇总数据的形式看待行为,而不是脱离它们在上下文中的发生方式。人类和社会科学家试图对媒体中捕获的行为进行建模,这些数据可以在来自媒体流的多维事件数据空间中表示。这些科学家(专家)的知识是一种宝贵的资源,可以用来搜索这个领域。我们提出了一种解决方案,将专家纳入迭代,交互式数据驱动的发现过程中,以发展所需的行为模型。我们用渐进的三层方法在多模态会议语料库上测试了我们的解决方案的准确性。
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