Skip to main content

Federico Angelini

Estimating abnormal human activity using multimodal surveillance.

Email: f.angelini2@ncl.ac.uk

Project title

Multimodal surveillance

Supervisors

Project description

This project focuses on Machine Learning and Fusion Techniques. The techniques will detect human activity using multimodal wide-area sensor measurements.

We will develop a robust model for video-based human activity. The model will estimate abnormal human actions. These will be based on multimodal data and contextual information.

The main techniques that we use include:

  • Deep Learning based architectures
  • multimodal data fusion
  • video data augmentation

The project involves extensive data recordings, software design, simulations and demo realisation.

We have achieved breakthrough results. These have led to:

  • academic publications
  • dataset recording
  • effective deep learning algorithms for human action classification and anomaly detection.

Publications

Interests

Artificial Intelligence, Deep Learning

Qualifications

  • BSc in Mathematics and MSc in Pure and Applied Mathematics, Università degli Studi di Roma “Tor Vergata”