ML/DL for Action Detection in Movies for Haptic Effects Generation

Haptic signals are sent to movie or home theater motorized seats in order to create an immersive environment to the viewer by applying movements and vibration to the seat. The haptic signals are currently created using a tedious manual design process. These signals need to be matched and synchronized to specific actions in the movie, such as gunshots or car crashes for instance. This project consists in developing machine learning models to automatically detect the appropriate actions in movies that need to be enhanced with haptic effects. The models will identify the events in the movie and provide their exact timing. This information will be used by the company to synthesize automatically the right haptic effect for each action detected.

Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

D-BOX Technologies Inc.

Discipline:

Computer science

Sector:

Artificial Intelligence

University:

Université de Montréal

Program: