4DVIDEO

4DVideo: 4D spatio-temporal modeling of real-world events from video streams

Coordinatore	EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	Switzerland [CH]
Totale costo	1˙757˙422 €
EC contributo	1˙757˙422 €
Programma	FP7-IDEAS-ERC Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	ERC-2007-StG
Funding Scheme	ERC-SG
Anno di inizio	2008
Periodo (anno-mese-giorno)	2008-08-01   -   2013-11-30

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH  Organization address address: Raemistrasse 101 city: ZUERICH postcode: 8092 contact info Titolo: Prof. Nome: Marc Cognome: Pollefeys Email: send email Telefono: -6323108 Fax: -6321742	CH (ZUERICH)	hostInstitution	0.00
2	EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH  Organization address address: Raemistrasse 101 city: ZUERICH postcode: 8092 contact info Titolo: Prof. Nome: Roland Cognome: Siegwart Email: send email Telefono: +41 44 632 5350 Fax: +41 44 632 1893	CH (ZUERICH)	hostInstitution	0.00

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 Obiettivo del progetto (Objective)

'The focus of this project is the development of algorithms that allow one to capture and analyse dynamic events taking place in the real world. For this, we intend to develop smart camera networks that can perform a multitude of observation tasks, ranging from surveillance and tracking to high-fidelity, immersive reconstructions of important dynamic events (i.e. 4D videos). There are many fundamental questions in computer vision associated with these problems. Can the geometric, topologic and photometric properties of the camera network be obtained from live images? What is changing about the environment in which the network is embedded? How much information can be obtained from dynamic events that are observed by the network? What if the camera network consists of a random collection of sensors that happened to observe a particular event (think hand-held cell phone cameras)? Do we need synchronization? Those questions become even more challenging if one considers active camera networks that can adapt to the vision task at hand. How should resources be prioritized for different tasks? Can we derive optimal strategies to control camera parameters such as pan, tilt and zoom, trade-off resolution, frame-rate and bandwidth? More fundamentally, seeing cameras as points that sample incoming light rays and camera networks as a distributed sensor, how does one decide which rays should be sampled? Many of those issues are particularly interesting when we consider time-varying events. Both spatial and temporal resolution are important and heterogeneous frame-rates and resolution can offer advantages. Prior knowledge or information obtained from earlier samples can be used to restrict the possible range of solutions (e.g. smoothness assumption and motion prediction). My goal is to obtain fundamental answers to many of those question based on thorough theoretical analysis combined with practical algorithms that are proven on real applications.'