Real-time shot segmentation of unedited video stream for maintenance work

F. Tsutsumi
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Abstract

We propose a shot segmentation method of live video stream for wearable assistants. This method can automatically divide an unedited video stream into meaningful shots, even if the stream contains shaking, vibration or blurring. The basic idea of the method is dividing video streams into "stationary" shots and "transitive" shots based on the tendency of visual changes. Two typical conventional methods were compared with our method on the 55 minute maintenance video recorded in a restricted environment. Analyzing the video and computing the recall and precision rate showed the adequacy of out method. Furthermore, the practical effectiveness of the methods was evaluated by another 54 minute patrol video in an outdoor environment. Nine human subjects tried to find randomly selected shots with different segmentation produced by three methods. The results show that our method supported users to find the shots most effectively (89% success in 5 minutes).
实时镜头分割未编辑的视频流维护工作
提出了一种用于可穿戴助手的实时视频流镜头分割方法。这种方法可以自动将未编辑的视频流分割成有意义的镜头,即使视频流包含抖动、振动或模糊。该方法的基本思想是根据视觉变化的趋势将视频流分为“静止”镜头和“传递”镜头。将两种典型的传统方法与我们的方法在受限环境下录制的55分钟维修视频进行比较。对视频进行分析,计算查全率和查准率,证明了该方法的有效性。此外,通过另一段54分钟的户外巡逻视频评估了这些方法的实际有效性。9名受试者试图找到随机选择的镜头,通过三种方法产生不同的分割。结果表明,我们的方法支持用户最有效地找到镜头(5分钟内成功率89%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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