ビデオ画像解析による運動連鎖の研究

The purpose of this study is to develop a video motion analysis system and to analyze the human kinetic chain during tennis service movements. The video digitizer and frame buffer were used to transfer the video picture to the computer. Polynomial regression curves of velocity were calculated from coordinate data. Relationships between maximal，minimum and points of inflection of racket head velocity and maximum wrist joint velocity were examined to solve the differential equations from velocity curves. The results of this study show that in this investigation that the phenomenon of maximum acceleration of a racket head caused by the negative acceleration of the wrist joint was associated with human kinetic chain

Web Video in Numbers - An Analysis of Web-Video Metadata

Web video is often used as a source of data in various fields of study. While specialized subsets of web video, mainly earmarked for dedicated purposes, are often analyzed in detail, there is little information available about the properties of web video as a whole. In this paper we present insights gained from the analysis of the metadata associated with more than 120 million videos harvested from two popular web video platforms, vimeo and YouTube, in 2016 and compare their properties with the ones found in commonly used video collections. This comparison has revealed that existing collections do not (or no longer) properly reflect the properties of web video "in the wild".Comment: Dataset available from http://download-dbis.dmi.unibas.ch/WWIN

Editorial: The role and influence of key stakeholders in the learning technology community

In this issue of ALT-J we have six articles that address three broad topics of video, costbenefit analysis and the emergent learning technology community. The first two articles present case studies on the use of video in teaching and learning. Shephard et al. describe their experiences of re-purposing a back 'care video' for video streaming, and use these experiences to highlight key challenges that others may face when attempting to re-purpose a video. Blake and Scanlon use their experiences of analysing video recordings of students, who were using computers to support their collaboration in solving statistical problems, to argue that such video analysis provides useful rich data with which to interpret and understand students' experiences

Video Tester -- A multiple-metric framework for video quality assessment over IP networks

This paper presents an extensible and reusable framework which addresses the problem of video quality assessment over IP networks. The proposed tool (referred to as Video-Tester) supports raw uncompressed video encoding and decoding. It also includes different video over IP transmission methods (i.e.: RTP over UDP unicast and multicast, as well as RTP over TCP). In addition, it is furnished with a rich set of offline analysis capabilities. Video-Tester analysis includes QoS and bitstream parameters estimation (i.e.: bandwidth, packet inter-arrival time, jitter and loss rate, as well as GOP size and I-frame loss rate). Our design facilitates the integration of virtually any existing video quality metric thanks to the adopted Python-based modular approach. Video-Tester currently provides PSNR, SSIM, ITU-T G.1070 video quality metric, DIV and PSNR-based MOS estimations. In order to promote its use and extension, Video-Tester is open and publicly available.Comment: 5 pages, 5 figures. For the Google Code project, see http://video-tester.googlecode.com

Video Time: Properties, Encoders and Evaluation

Time-aware encoding of frame sequences in a video is a fundamental problem in video understanding. While many attempted to model time in videos, an explicit study on quantifying video time is missing. To fill this lacuna, we aim to evaluate video time explicitly. We describe three properties of video time, namely a) temporal asymmetry, b)temporal continuity and c) temporal causality. Based on each we formulate a task able to quantify the associated property. This allows assessing the effectiveness of modern video encoders, like C3D and LSTM, in their ability to model time. Our analysis provides insights about existing encoders while also leading us to propose a new video time encoder, which is better suited for the video time recognition tasks than C3D and LSTM. We believe the proposed meta-analysis can provide a reasonable baseline to assess video time encoders on equal grounds on a set of temporal-aware tasks.Comment: 14 pages, BMVC 201