Project

VIDETE

Generation of prior knowledge with the help of learning systems for 4D analysis of complex scenes

Generation of prior knowledge with the help of learning systems for 4D analysis of complex scenes

  • Duration:

Motivation

Artificial intelligence currently influences many areas, including machine vision. For applications in the fields of autonomous systems, medicine and industry there are fundamental challenges: 1) generating prior knowledge to solve severely under-determined problems, 2) verifying and explaining the response calculated by the AI, and 3) providing AIs in scenarios with limited computing power.

Goals and Procedure

The goal of VIDETE is to use AI to generate prior knowledge using machine learning processes, thus making previously unsolvable tasks such as the reconstruction of dynamic objects practically manageable with just one camera. With suitable prior knowledge it will be easier to analyze and interpret general scenes, for example in the area of autonomous systems, with the help of algorithms. Furthermore, methods will be developed to justify the calculated results before they are used further. In the field of medicine this would be comparable to the opinion of a colleague in contrast to the general answers of current AI methods. A key technique is considered to be the modularization of algorithms, which will especially increase the availability of AI. Modular components can be realized efficiently in hardware. Thus, calculations (e.g. the recognition of a gesture) can be performed close to the generating sensor. This, in turn, enables semantically enriched information to be communicated with low overhead, which means that AI can also be used on mobile devices with low resources available.

Innovations and Perspectives

Artificial intelligence finds its way into almost all areas of daily life and work. The results expected from the VIDETE project will be independent of the defined research scenarios and can contribute to progress in many application areas (private life, industry, medicine, autonomous systems, etc.).

Sponsors

Federal Ministry of Education and Research

Reference Number: 01IW18002

Federal Ministry of Education and Research

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Contact Person
Dr. rer. nat. Dipl.-Inf. Gerd Reis

Keyfacts

Publications about the project

Kripasindhu Sarkar, Didier Stricker

In: International Conference on Pattern Recognition Applications and Methods. International Conference on Pattern Recognition Applications and Methods (ICPRAM-2019) February 19-21 Prague Czech Republic Scitepress 2019.

To the publication
Vladislav Golyanik, André Jonas, Didier Stricker

In: International Conference on Machine Vision Applications (MVA). IAPR Conference on Machine Vision Applications (MVA-2019) May 27-31 Tokyo Japan IAPR 2019.

To the publication
Soshi Shimada, Vladislav Golyanik, Christian Theobalt, Didier Stricker (editor)

International Conference on Computer Vision and Pattern Recognition (CVPR-2019) Photogrammetric Computer Vision Workshop June 16-20 Long Beach CA United States 2019.

To the publication

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz