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Video Based Online Behavior Detection Using Probabilistic Multi Stream Fusion

机译:基于概率多流融合的基于视频的在线行为检测

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In the present treatise, we propose an approach for a highly configurable image based online person behaviour monitoring system. The particular application scenario is a crew supporting multi-stream on-board threat detection system, which is getting more desirable for the use in public transport. For such frameworks, to work robust in mostly unconstrained environments, many subsystems have to be employed. Although the research field of pattern recognition has brought up reliable approaches for several involved sub-tasks in the last decade, there often exists a gap between reliability and the needed computational efforts. However in order, to accomplish this highly demanding task, several straight forward technologies, here the output of several so-called weak classifiers using low-level features are fused by a sophisticated Bayesian network
机译:在本论文中,我们提出了一种基于高度可配置图像的在线人行为监测系统的方法。特定的应用场景是支持多流车载威胁检测系统的机组人员,这对于在公共交通中的使用越来越受欢迎。对于这样的框架,要在大多数不受限制的环境中稳定运行,必须使用许多子系统。尽管模式识别的研究领域在过去的十年中为几种涉及的子任务提出了可靠的方法,但是在可靠性和所需的计算工作之间经常存在差距。但是,为了完成这项艰巨的任务,需要使用一些简单的技术,例如使用复杂的贝叶斯网络将几种使用低级特征的所谓弱分类器的输出融合在一起。

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