Thesis Code: 19016
Thesis Type: M.Sc. thesis in Machine Learning, Data Science, Computer Science, Mathematics, or equivalent

Research Area: Data Science for Industrial and Societal Application


  • Knowledge of Python
  • Software development skills
  • Basic concepts on data science, concerning data analysis, processing and machine learning
  • Basic concepts on image processing

Predicting pedestrian’s future path is important for both self-driving cars and security systems. In fact, it allows to prevent dangerous situations such as traffic accidents between cars and people or collision between autonomous robot and people. Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions.
The objective of this thesis consists in the study and implementation of machine learning algorithms useful for predicting the path a pedestrian will take in the successive frames of a video sequence. The proposed algorithms will be trained using a synthetic dataset and open datasets. This is created with the help of dedicated tools for extracting information from the Grand Theft Auto V graphic engine. The candidate will have both the task of creating the dataset and studying and evaluating the best algorithms to apply for the case of study. The type of algorithms being studied and tested are to be classified among those of video analysis, with the exploratory possibility of deep learning algorithms using popular framework (TensorFlow, PyTorch, Keras, etc…).

Contact: send a resume with attached the list of exams to specifying the thesis code and title.