apidis develops cost-effective solutions for autonomous and/or personalized production of video summaries for controlled scenarios (sports events or surveillance).
Democratic and personalized production of multimedia content is one of the most exciting challenges that
content providers will have to face in the near future. APIDIS has addressed this challenge by
proposing a framework to automate the collection and distribution of digital content.
As a federating objective, APIDIS targets cost-effective autonomous production,
so as to make the creation of audiovisual reports profitable, even in case of small- or medium-size audience.
First, APIDIS is investigating the automatic extraction of intelligent content from
networks of multi-modal sensors. Intelligence refers here to the identification of salient segments within the audiovisual content,
using distributed scene analysis algorithms.
Second, APIDIS exploits that knowledge to
automate the production of video content for specific scenarios, e.g. most notably sports events or surveillance. It considers
personalized and potentially interactive content summarization mechanisms, to address professional and non-professional needs.
The potential applications of the integrated technology and methodologies developed within APIDIS
are numerous, ranging from personalized access to local sport events through a web portal or a mobile hand-set; cost-effective
and fully automated production of content dedicated to small-audience, e.g. souvenirs DVD, university lectures, etc; but also
interactive browsing and automated summarization for video surveillance.
The results of this project will soon be exploited through a startup that will propose the system as a whole with the associated services. Please contact the coordinator if you are interested in acquiring parts of the project results. He will forward your request to the partners that own of the corresponding IPR.
Access to the first acquisition campaign of basket ball
data during the project.
Related publications, to refer to when using the dataset:
C. De Vleeschouwer, Fan Chen, D. Delannay, C. Parisot, C. Chaudy, E. Martrou, A. Cavallaro, Distributed video acquisition and annotation for sport-event summarization, NEM Summit, Saint Malo, France, 2008.
Fan Chen, D. Delannay, C. De Vleeschouwer, An Autonomous Framework to Produce and Distribute Personalized Team-Sport Video Summaries: A Basketball Case Study, IEEE Transactions on Multimedia, Volume:13 , Issue: 6, pp. 1381 - 1394, December 2011.
co-funded by the European Union