New methods of machine learning and artificial intelligence, combined with precise real-time tracking, allow a new depth of game and training analysis.
Automated action recognition in combination with machine learning and hierarchical event recognition allows the automatic generation of game statistics.
Real-time analysis of soccer players' performance and the course of play using cost-effectiv sensors and machine learning to detect speeds, actions and events for trainers and players.
Real-time measurements of the acceleration curve and speed of punches using specially developed miniaturized sensor technology in combination with sensor-based artificial intelligence.
Fast sports-scene search engine. Based on deep learning, our S3Engine technology enables fast searches for similar game scenes in sports, evaluates them and suggests solutions.
The retrofittable and intelligent sensor module for hand tools measures various parameters in manual work processes and thus enables process optimization.
As part of the QACI consortium (Quantum Algorithms for Application, Cloud & Industry), we are researching machine learning-based approaches and methods for optimal use of NISQ hardware through optimized compilation.
Im Bayerischen Kompetenzzentrum Quanten Security and Data Science (BayQS) erforschen wir den Einsatz von Quantencomputern zur Verbesserung von maschinellem Lernen.
In the QuaST research project, we are developing tools to improve the exploitation of quantum computers and paving the way for distributed quantum computing.
EDIH Digital Innovations for Industry in Bavaria (DIBI)
The European Digital Innovation Hubs (EDIHs) are part of the European Union's "The Digital Europe Programme". A nationwide network of innovation centers in the EU is intended to support the digital transformation of administration and business. Fraunhofer IIS is a project partner of EDIH DIBI, one of Bavaria's three innovation centers to date.
Fraunhofer Institute for Integrated Circuits IIS