An intelligent vision-based system for detecting and classifying human behaviors

The funding comes from grants in collaboration with the industry. Contact the professor for further information.

Self-harm is a common cause of death in correctional settings. The constant monitoring and the rapid detection of self-harm events are crucial for protecting inmates’ lives and reducing the mortality rates. During the last decades, several technologies have been developed for preventing suicide attempts in prisons. However, most of them use cumbersome devices and/or are greatly depending on human guard attention and intervention. Our objective is to develop autonomous video surveillance solutions in order to intelligently detect life-threatening events in prisons, and allow a rapid and reactive intervention. These objectives will be achieved by ensuring the efficient exploitation of recent advances in visual acquisition technologies, computer vision methods and artificial intelligence.

We plan to develop computer vision methods for inmate gait analysis and fall detection. We will base our work on existent methods that are mostly developed in the context of neurodegenerative diseases and elderly people monitoring. We plan to extract and measure human gait features such as a center of gravity change, a pace length variation, and a joint angle variation. We plan to use these gait features for constructing a motion model in order to detect unsteady walking and falls. The detection of these two events are closely related, because gait features can be used as an important indicator to detect falls, by analyzing the balance of the centroid of the human body. Approaches based on machine learning will be used for gait analysis and fall detection by using existing datasets.
Our work will allow our industrial partner to develop a commercial product to be deployed in correctional services. The impact of this product is expected to help saving the lives of incarcerated people at risk, and avoid the resulting suffering.

Connaissances requises

The candidate must demonstrate:

  • Knowledge or interest in artificial intelligence algorithms
  • Excellent motivation
  • Good research record and/or good academic curriculum
  • Ability to work as a team and independently
  • Ability to communicate well in writing
  • Excellent programming knowledge

Programme d'études visé

Maîtrise avec mémoire, Doctorat

Domaines de recherche

Technologies de l'information et des communications

Financement

A scholarship is available.

Autres informations

Projet affiché jusqu'à la fin de la session d'automne

Personne à contacter

Rita Noumeir | rita.noumeir@etsmtl.ca