dc.contributor.author | Arpatzoglou, Vasiliki | en_US |
dc.contributor.author | Kardara, Artemis | en_US |
dc.contributor.author | Diehl, Alexandra | en_US |
dc.contributor.author | Flueckiger, Barbara | en_US |
dc.contributor.author | Helmer, Sven | en_US |
dc.contributor.author | Pajarola, Renato | en_US |
dc.contributor.editor | Agus, Marco and Garth, Christoph and Kerren, Andreas | en_US |
dc.date.accessioned | 2021-06-12T11:03:30Z | |
dc.date.available | 2021-06-12T11:03:30Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 978-3-03868-143-4 | |
dc.identifier.uri | https://doi.org/10.2312/evs.20211057 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/evs20211057 | |
dc.description.abstract | Analyzing body movement as a means of expression is of interest in diverse areas, such as dance, sports, films, as well as anthropology or archaeology. In particular, in choreography, body movements are at the core of artistic expression. Dance moves are composed of spatial and temporal structures that are difficult to address without interactive visual data analysis tools. We present a visual analytics solution that allows the user to get an overview of, compare, and visually search dance move features in video archives. With the help of similarity measures, a user can compare dance moves and assess dance poses. We illustrate our approach through three use cases and an analysis of the performance of our similarity measures. The expert feedback and the experimental results show that 75% to 80% of dance moves can correctly be categorized. Domain experts recognize great potential in this standardized analysis. Comparative and motion analysis allows them to get detailed insights into temporal and spatial development of motion patterns and poses. | en_US |
dc.publisher | The Eurographics Association | en_US |
dc.subject | Human | |
dc.subject | centered computing | |
dc.subject | Visual analytics | |
dc.title | DanceMoves: A Visual Analytics Tool for Dance Movement Analysis | en_US |
dc.description.seriesinformation | EuroVis 2021 - Short Papers | |
dc.description.sectionheaders | Analytics and Applications | |
dc.identifier.doi | 10.2312/evs.20211057 | |
dc.identifier.pages | 67-71 | |