Real-time video analysis and personalized media streaming environments for large scale athletic events

Papaoulakis, N; Doulamis, N; Patrikakis, C; Soldatos, J; Pnevmatikakis, A; Protonotarios, E

dc.contributor.author	Papaoulakis, N	en
dc.contributor.author	Doulamis, N	en
dc.contributor.author	Patrikakis, C	en
dc.contributor.author	Soldatos, J	en
dc.contributor.author	Pnevmatikakis, A	en
dc.contributor.author	Protonotarios, E	en
dc.date.accessioned	2014-03-01T02:51:47Z
dc.date.available	2014-03-01T02:51:47Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35653
dc.subject	Ambient camera selection	en
dc.subject	Personalized media streaming	en
dc.subject	Real time context awareness	en
dc.subject	User-centric networking	en
dc.subject.other	Ambient camera selection	en
dc.subject.other	Camera selection	en
dc.subject.other	Context- awareness	en
dc.subject.other	Real time	en
dc.subject.other	User-centric	en
dc.subject.other	Acoustic streaming	en
dc.subject.other	Cameras	en
dc.subject.other	Management	en
dc.subject.other	Neural networks	en
dc.subject.other	Technical presentations	en
dc.subject.other	Video streaming	en
dc.subject.other	Media streaming	en
dc.title	Real-time video analysis and personalized media streaming environments for large scale athletic events	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/1463542.1463560	en
heal.identifier.secondary	http://dx.doi.org/10.1145/1463542.1463560	en
heal.publicationDate	2008	en
heal.abstract	This paper presents the architecture of the My-e-Director 2012, an FP7 call-1 European funded project in the objective area of Networked Media. My-e-Director 2012 is a unique interactive broadcasting platform, which enables end-users to have access on innovative services, like selection of focal actors/scenes and points of interest within real-time broadcasted streams. Emphasis is placed on the ambient camera selection module, which is the heart of the My-e-Director project. The ambient camera selection is responsible for detecting athletes in large-scale Olympic events. Then, this information is fed back to the system in order to allow for personalized video steaming and delivery services. Since detection of human in athletic event is a very demanding task, our method is based on a retrainable neural network architecture, which non-linearly models the color and texture properties of the object of interest. The retraining algorithm adapts the neural network model to fit the current environmental conditions, while simultaneously trusts as much as possible the previous information. The neural network classifier is combined with a conventional motion-based tracking scheme, which provides accurate contour detection. Aspects of multiple camera configurations are discussed. Copyright 2008 ACM.	en
heal.journalName	MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops	en
dc.identifier.doi	10.1145/1463542.1463560	en
dc.identifier.spage	105	en
dc.identifier.epage	111	en