VIDEOLEARN
Video and 3D Analysis for Visual Learning
| Coordinatore | ALBERT-LUDWIGS-UNIVERSITAET FREIBURG
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | Germany [DE] |
| Totale costo | 1˙462˙800 € |
| EC contributo | 1˙462˙800 € |
| 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-2011-StG_20101014 |
| Funding Scheme | ERC-SG |
| Anno di inizio | 2012 |
| Periodo (anno-mese-giorno) | 2012-01-01 - 2016-12-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
ALBERT-LUDWIGS-UNIVERSITAET FREIBURG
Organization address
address: FAHNENBERGPLATZ contact info |
DE (FREIBURG) | hostInstitution | 1˙462˙800.00 |
| 2 |
ALBERT-LUDWIGS-UNIVERSITAET FREIBURG
Organization address
address: FAHNENBERGPLATZ contact info |
DE (FREIBURG) | hostInstitution | 1˙462˙800.00 |
Mappa
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Obiettivo del progetto (Objective)
'Reliable recognition of thousands of object and action categories is today's key challenge in computer vision. Most contemporary approaches are based on supervised learning algorithms to train object classifiers. While manual annotation has become easier in recent years, it is still not scalable to a large set of categories. Moreover, as it is usually based on human language it does not reflect the visual characteristics of objects, but tries to establish high-level links that should actually be learned after appropriate visual features have been captured.
In this proposal, we aim at reducing the manual labeling effort by making use of the natural organization of visual data as it is provided by a video stream. In the same setting, we also aim at learning a more sophisticated structural representation of objects. Rather than manually specifying parts and attributes of objects that have a counterpart in language, we will seek correlated visual patterns by letting the data speak. Exploiting the natural arrangement of images in video and the inherent 3D scene structure is decisive, since weakly correlated images as obtained from photo collections might not contain rich enough relationship information.
We will also consider the active observer setting, i.e., where the camera can be moving. This allows extracting far more information, but also requires detailed control of the low-level and mid-level computer vision techniques involved, particularly motion estimation and tracking. The importance of these components is often underestimated in contemporary visual learning approaches.
Apart from the impact on the field of computer vision itself, the improved performance in visual recognition that we anticipate in this project has direct consequences for many important applications, particularly automotive systems and robotics, where the use of visual sensory input is more and more considered one of the most important components of future systems.'