Intelligent surveillance for event detection

We aim to develop a video surveillance system that detects and reports events of interest to users. Traditional way is to use RGB cameras to operate in constantly and evenly illuminated areas to detect simple events. The challenge is that visual characteristics of an unconstrained scene are unstable due to illumination variations. In this project, by using RGB-D cameras, 3D position information of humans can be obtained, which is robust and insensitive to the illumination variations. Event detection is realized by machine learning models using human joint positions as features, since joint positions are suitable for event classification. An intelligent and robust real-time event detection system is to be developed in this project.

Faculty Supervisor:

Rita Noumeir

Student:

Bo Li

Partner:

Aerosystems International Inc.

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program: