Multiple trajectory prediction. Blue: past, red: futures.

The project aims to develop computer vision solutions based on supervised machine learning that support behavior analysis and prediction of actions and scene evolution for the purpose of autonomous driving. This is a one-year project funded by IMRA Europe.

Understanding and predicting behaviors is one of the hardest task to solve with computer vision. Advances in computer vision driven by the availability of powerful GPU-based computational support and the achievements in deep network technology have provided encouraging results that make this problem technically addressable with reasonable expectations of effective implementations in the real world. Particularly, Long Short Term Memory networks (LSTM) are a special kind of recurrent network, capable of reasoning about previous events in a temporal sequence to inform later ones, and learning long-term dependencies. In this project we tackled the challenging problem of image inpainting in the semantic space, automatic trajectory extraction from dash cam data and finally we developed a memory based network for trajectory prediction.

Lorenzo Seidenari
Lorenzo Seidenari
Assistant Professor of Computer Engineering

I am an Assistant Professor (Tenure Track) of Computer Engineering at the University of Florence working on Deep Learning and Computer Vision.