Portfolio – Page 8

DigiTwin4CIUE – Digital Twins for Complex Infrastructures and Urban Ecosystems

The Digital Twin for Complex Infrastructures and Urban Ecosystems is an advanced and specialized academic offering designed primarily for industry professionals looking to expand their skills in the rapidly evolving landscape of civil engineering technology. The principal objective of the program is to equip professionals with the necessary tools and knowledge to harness the power of digital twin technologies and apply them in their respective fields to enhance the design, operation, and maintenance of civil infrastructures and urban ecosystems.

The digital twin technology, characterized by the creation of a dynamic virtual clone of a physical entity is quickly becoming a game-changer in the field of civil engineering. It adds to increased efficiency and resilience, as well as cost optimization, by facilitating real-time monitoring, predictive analysis, and informed decision-making processes. The program’s focus on this emerging technology guarantees the delivery of cutting-edge knowledge and practical skills, placing our alumni as market leaders.

Duration: 2022-2025

People Detection and Tracking for Social Robots and Autonomous Cars

Research ProjectsBy AIMAS October 12, 2021

Detecting, tracking, and recognizing people is a valuable capability for machines. However, these tasks are quite difficult to be achieved autonomously and, although significant results have been obtained, they are still a major technological challenge. People tracking, unlike other recognition and interpretation tasks, is difficult both from the point of view of the recognition and prediction of trajectory, and from the one of the identifications of the ground truth.

The main objective of the PETRA project is the development of a software platform enabling the development of applications requiring people detection and tracking in real environments. The design and implementation of the platform and the set of supported algorithms for people detection and tracking will be such that they can be easily used and integrated in tasks performed by social robots in closed spaces and in tasks in open spaces, such as the case for pedestrian detection and tracking. The scientific and technological challenge of the project is to start from our current developments on people detection and tracking in the contexts of user-robot interaction and autonomous driving to develop and implement novel solutions based on deep learning approaches. One of the project challenges is to extensively test the implementations towards several difficult benchmarks, but also on our own data sets, and strive to achieve results better than current state-of-the-art.

Duration: 2020-2022