SportSense: user interface for sketch-based spatio-temporal team sports video scene retrieval
Version
Published
Date Issued
2018
Author(s)
Probst, Lukas
Al Kabary, Ihab
Lobo, Rufus
Rauschenbach, Fabian
Schuldt, Heiko
Seidenschwarz, Philipp
Editor(s)
Said, Alan
Type
Conference Paper
Language
English
Subjects
Abstract
In the last years, various sports have seen significant efforts in collecting large volumes of video, mainly for analytical purposes. These videos are tagged with events that include spatial and temporal information. In order to avoid that game analysts have to manually analyze large video collections, appropriate user interfaces are needed for finding characteristic video scenes. SPORTSENSE is a novel sketch-based video retrieval system tailored to the needs of sports video analysts which include spatio-temporal queries such as player movements, ball trajectories, or interactions between players. We present the user interface of SPORTSENSE that allows users to draw sketches of spatio-temporal events on the field and that visualizes the retrieved video scenes. We introduce the data model
of SPORTSENSE, show the sketch-based spatio-temporal retrieval on the annotated videos, and present a qualitative evaluation exhibiting the effectiveness of the SPORTSENSE UI.
of SPORTSENSE, show the sketch-based spatio-temporal retrieval on the annotated videos, and present a qualitative evaluation exhibiting the effectiveness of the SPORTSENSE UI.
Series/Report No.
CEUR Workshop Proceedings
ISSN
1613-0073
Publisher URL
Volume
2068
Conference
IUI 2018 Workshop on User Interfaces for Spatial and Temporal Data Analysis (UISTDA 2018)
Publisher
RWTH
Submitter
ServiceAccount
Citation apa
Probst, L., Al Kabary, I., Lobo, R., Rauschenbach, F., Schuldt, H., Seidenschwarz, P., & Rumo, M. (2018). SportSense: user interface for sketch-based spatio-temporal team sports video scene retrieval (A. Said, Ed.; Vol. 2068). RWTH. https://doi.org/10.24451/arbor.10443
File(s)![Thumbnail Image]()
Loading...
restricted
Name
Rumo_SportSense_UISTDA_2018_Paper.pdf
Size
1.29 MB
Format
Adobe PDF
Checksum (MD5)
dee1f56ccedae2fab3d68c13147957ea
