Researcher in Computer Vision for Sports Analytics.
Ph.D. student specializing in Video Understanding,
Multimodal Learning, and AI-driven Analytics.
Areas of Interest
Computer Vision
Expertise in video-based action recognition, pose estimation, and egocentric video analysis for event classification and human behavior understanding.
Multimodal Learning
Integration of RGB data, pose estimation, and textual information through Vision-Language Models (VLMs) and Large Language Models (LLMs) to build context-aware AI systems.
AI for Sports Performance
Development of AI-driven systems for analyzing athletic performance, providing real-time feedback, proficiency estimation, and strategic insights from video and pose data.
The Free University of Bolzano, EMG Italy, Fondazione Bruno Kessler, Small Pixels, and Vodafone Italy successfully collaborated to transform the analysis, experience, and broadcasting of volleyball through cutting-edge technologies. By leveraging 5G, Artificial Intelligence, and Video Analytics Systems, the project redefined how sports are observed and understood in real time.
Gate-Shift-Pose (GSP) is a multimodal architecture designed to enhance action recognition in sports by integrating skeleton-based pose information with RGB frames. The model builds on the Gate-Shift-Fuse (GSF) network and introduces early- and late-fusion strategies to better capture the intricate motion dynamics of sports.
This project enables real-time object detection using different versions of YOLO (You Only Look Once) in conjunction with the Meta Project Aria Glasses. It allows streaming video input via USB or WiFi and performs object detection on the streamed data.
GIVA is a vocal assistant that combines speech recognition and text-to-speech with the capabilities of GPT. Prompts are engineered so that GPT provides outputs that are short and adapted to be converted to audio.
NLP project focused on the Semantic Representation of text. Different techniques are proposed and compared, and a final web app classifies comments on demand.
Data Integration project focused on theme park accident analysis leveraging Semantic Web and Linked Data technologies. A web app with interactive visualizations allows the exploration of the results.
Laura Meneghetti, Edoardo Bianchi, Nicola Demo, Gianluigi Rozza. Published in: 2025 Workshop on Design and Architectures for Signal and Image Processing (HiPEAC 2025).