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Crowd-based Semantic Event Detection and Video Annotation for Sports Videos

Sulser, Fabio and Giangreco, Ivan and Schuldt, Heiko. (2014) Crowd-based Semantic Event Detection and Video Annotation for Sports Videos. In: CrowdMM '14 : proceedings of the International Workshop on Crowdsourcing for Multimedia, November 7, 2014, Orlando, Florida, USA. New York, pp. 63-68.

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Official URL: http://edoc.unibas.ch/dok/A6298875

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Abstract

Recent developments in sport analytics have heightened the interest in collecting data on the behavior of individuals and of the entire team in sports events. Rather than using dedicated sensors for recording the data, the detection of semantic events reflecting a team's behavior and the subsequent annotation of video data is nowadays mostly performed by paid experts. In this paper, we present an approach to generating such annotations by leveraging the wisdom of the crowd. We present the CrowdSport application that allows to collect data for soccer games. It presents crowd workers short video snippets of soccer matches and allows them to annotate these snippets with event information. Finally, the various annotations collected from the crowd are automatically disambiguated and integrated into a coherent data set. To improve the quality of the data entered, we have implemented a rating system that assigns each worker a trustworthiness score denoting the confidence towards newly entered data. Using the DBSCAN clustering algorithm and the confidence score, the integration ensures that the generated event labels are of high quality, despite of the heterogeneity of the participating workers. These annotations finally serve as a basis for a video retrieval system that allows users to search for video sequences on the basis of a graphical specification of team behavior or motion of the individual player. Our evaluations of the crowd-based semantic event detection and video annotation using the Microworkers platform have shown the effectiveness of the approach and have led to results that are in most cases close to the ground truth and can successfully be used for various retrieval tasks.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenbanken (Schuldt)
UniBasel Contributors:Schuldt, Heiko and Giangreco, Ivan and Sulser, Fabio and Sulser, Fabio
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Bibsysno:Link to catalogue
Publisher:ACM
ISBN:978-1-4503-3128-9
Note:Publication type according to Uni Basel Research Database: Conference paper
Language:English
Identification Number:
Last Modified:07 May 2018 08:36
Deposited On:05 Jun 2015 08:53

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