SCOVIS

Self-Configurable Cognitive Video Supervision

 Coordinatore INSTITUTE OF COMMUNICATION AND COMPUTER SYSTEMS 

 Organization address address: Heroon Polytecnhiou street 9
city: Zografou, Athens
postcode: 157 73

contact info
Titolo: Prof
Nome: Yannis
Cognome: Vassiliou
Email: send email
Telefono: +30210 772 4374
Fax: +30210 772 2527

 Nazionalità Coordinatore Greece [EL]
 Totale costo 3˙793˙030 €
 EC contributo 2˙750˙000 €
 Programma FP7-ICT
Specific Programme "Cooperation": Information and communication technologies
 Code Call FP7-ICT-2007-1
 Funding Scheme CP
 Anno di inizio 2008
 Periodo (anno-mese-giorno) 2008-03-01   -   2011-02-28

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    INSTITUTE OF COMMUNICATION AND COMPUTER SYSTEMS

 Organization address address: Heroon Polytecnhiou street 9
city: Zografou, Athens
postcode: 157 73

contact info
Titolo: Prof
Nome: Yannis
Cognome: Vassiliou
Email: send email
Telefono: +30210 772 4374
Fax: +30210 772 2527

EL (Zografou, Athens) coordinator 0.00
2    ATOS ORIGIN SOCIEDAD ANONIMA ESPANOLA

 Organization address address: CALLE DE ALBARRACIN 25
city: MADRID
postcode: 28037

contact info
Titolo: Mr.
Nome: Santiago
Cognome: Ristol
Email: send email
Telefono: -4861877
Fax: -4860510

ES (MADRID) participant 0.00
3    EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH

 Organization address address: RAEMISTRASSE 101
city: ZUERICH
postcode: 8092

contact info
Titolo: Prof.
Nome: Luc
Cognome: Van Gool
Email: send email
Telefono: +41-44-632 6578
Fax: +41-44-632 1199

CH (ZUERICH) participant 0.00
4    JOANNEUM RESEARCH FORSCHUNGSGESELLSCHAFT MBH

 Organization address address: STEYRERGASSE 17
city: GRAZ
postcode: 8010

contact info
Titolo: DI
Nome: Werner
Cognome: Haas
Email: send email
Telefono: 433169000000
Fax: 433169000000

AT (GRAZ) participant 0.00
5    KATHOLIEKE UNIVERSITEIT LEUVEN

 Organization address address: OUDE MARKT 13
city: LEUVEN
postcode: 3000

contact info
Titolo: Ir.
Nome: Maria
Cognome: Vereeken
Email: send email
Telefono: +32 16 326504
Fax: +32 16 326515

BE (LEUVEN) participant 0.00
6    NISSAN MOTOR IBERICA SA

 Organization address address: ZONA FRANCA SECTOR B CALLE
city: BARCELONA
postcode: 8040

contact info
Nome: N/A
Cognome: N/A
Email: send email
Telefono: +00 0 000000

ES (BARCELONA) participant 0.00
7    UNIVERSITY OF SOUTHAMPTON

 Organization address address: HIGHFIELD CAMPUS
city: SOUTHAMPTON
postcode: SO17 1BJ

contact info
Titolo: Dr
Nome: Colin
Cognome: Upstill
Email: send email
Telefono: 442381000000
Fax: 442381000000

UK (SOUTHAMPTON) participant 0.00

Mappa


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models    scovis    effort    camera    model    descriptors    data    automatically    supervised    self    object    active    unsupervised    ing    visual    learning    significantly    monitoring    enhanced   

 Obiettivo del progetto (Objective)

SCOVIS will investigate weakly supervised learning algorithms and self-adaptation strategies for analysis of visually observable procedures. SCOVIS research directly affects ease of deployment and minimises effort of operation of monitoring systems and is unique in the sense that it links object learning using low-level object descriptors and procedure learning with adaptation mechanisms and active camera network coordination. SCOVIS advocates a synergistic approach that combines largely unsupervised learning and model evolution in a bootstrapping process; it involves continuous learning from visual content in order to enrich the models and, inversely, the direct use of these models to enhance the extraction. In the SCOVIS application scenario user interaction will be significantly reduced compared to current methods. The system will be able to calculate the camera spatial relations automatically (self-configuration) for coupled, uncoupled and active cameras. The user will define a set of objects and procedures of interest during a very short supervised learning phase, while the associations with low-level descriptors will be automatically learnt. The resulting models will be significantly enhanced through online data acquisition and unsupervised learning (adaptation). The enhanced models will be able to be verified and potentially adapted through relevance feedback. The main measurable objective of SCOVIS will be to significantly improve the versatility and the performance of current monitoring systems. The resulting technology will enable the easy installation of intelligent supervision systems, which has not been possible so far, due to the prohibitively high manual effort and the inability to model complex visual processes. The produced technology will be evaluated through realistic scenarios related to industry and public infrastructure. The proposed research will be performed with absolute respect to privacy and personal data of monitored individuals.

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