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Detection of Moving Objects in Surveillance Video by Integrating Bottom-up Approach with Knowledge Base

机译:通过自下而上的方法与知识库相结合来检测监控视频中的运动对象

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In the modern age, where every prominent and populous area of a city is continuously monitored, a lot of data in the form of video has to be analyzed. There is a need for an algorithm that helps in the demarcation of the abnormal activities, for ensuring better security. To decrease perceptual overload in CCTV monitoring, automation of focusing the attention on significant events happening in overpopulated public scenes is also necessary. The major challenge lies in differentiating detecting of salient motion and background motion. This paper discusses a saliency detection method that aims to discover and localize the moving regions for indoor and outdoor surveillance videos. This method does not require any prior knowledge of a scene and this has been verified with snippets of surveillance footages.
机译:在现代时代,城市的每个人口稠密地区都受到不间断的监控,因此必须分析大量视频形式的数据。需要一种算法来帮助划分异常活动,以确保更好的安全性。为了减少CCTV监视中的感知过载,还需要自动将注意力集中在人满为患的公共场景中发生的重大事件上。主要挑战在于区分显着运动和背景运动的检测。本文讨论了一种显着性检测方法,旨在发现和定位室内和室外监控视频的移动区域。这种方法不需要任何场景的先验知识,并且已经通过监视录像片段的验证。

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