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Information assimilation framework for event detection in multimedia surveillance systems

机译:多媒体监视系统中用于事件检测的信息同化框架

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Most multimedia surveillance and monitoring systems nowadays utilize multiple types of sensors to detect events of interest as and when they occur in the environment. However, due to the asynchrony among and diversity of sensors, information assimilation - how to combine the information obtained from asynchronous and multifarious sources is an important and challenging research problem. In this paper, we propose a framework for information assimilation that addresses the issues - "when", "what" and "how" to assimilate the information obtained from different media sources in order to detect events in multimedia surveillance systems. The proposed framework adopts a hierarchical probabilistic assimilation approach to detect atomic and compound events. To detect an event, our framework uses not only the media streams available at the current instant but it also utilizes their two important properties - first, accumulated past history of whether they have been providing concurring or contradictory evidences, and - second, the system designer's confidence in them. The experimental results show the utility of the proposed framework.
机译:如今,大多数多媒体监视和监视系统都使用多种类型的传感器来检测感兴趣的事件以及何时在环境中发生。然而,由于传感器之间的异步性和多样性,信息同化-如何组合从异步和多种来源获得的信息是一个重要且具有挑战性的研究问题。在本文中,我们提出了一个信息同化框架,该框架解决了以下问题:“何时”,“什么”和“如何”同化从不同媒体来源获得的信息,以便检测多媒体监视系统中的事件。所提出的框架采用分级概率同化方法来检测原子和复合事件。为了检测事件,我们的框架不仅使用当前可用的媒体流,而且还利用了它们的两个重要属性-首先,积累它们是否提供了一致或矛盾的证据的历史记录,-其次,系统设计者的对他们充满信心。实验结果表明了该框架的实用性。

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