2018 COE Engineering Design Project (XF02)

Faculty Lab Coordinator

Xavier Fernando

Topic Category

Software / Intelligent Instrumentation


According to Securehouse.Ca there are 159338 burglaries reported in Canada in 2015 alone. That means one out of every 28 houses are burglarized. This is unacceptable. Although many houses have installed surveillance cameras to prevent burglaries, the cameras are not effective when it is dark. Can we do advanced video processing to automatically identify thieves before a burglary and alert the officials. This is the objective of this project.


To develop an object identification and tracking algorithm in low light video environment that should alert the authorities.

Partial Specifications

Video processing in dark environments is in its preliminary stages. Nevertheless, it has enormous potential, since nowadays many buildings are equipped with video surveillance systems. Some work has been done in detecting and tracking human movements in videos. However, this is very difficult in low-light artificial illumination conditions with uneven lighting distribution. There are mainly black, or gray tones at night times and often the intruder’s clothes are dirty and dark colored which easily blends with the background in low illumination.

Suggested Approach

This project requires extensive computing. The students will be given an account in Southern Ontario Smart Computing Platform (SOSCIP). They can run deep learning algorithms in the powerful SOSCIP platform for object identification in low light environments. This is basically a Linux environment. I do have a Post Doctoral Fellow working in this area. He will guide the students to get started in supercomputing platform.

Group Responsibilities

The group is responsible for the successful completion of the overall project.

Student A Responsibilities

Deep learning algorithms

Student B Responsibilities

Video Signal Processing

Student C Responsibilities

Object tracking in low light environment

Course Co-requisites


XF02: BURGLAR DETECTION IN DARK | Xavier Fernando | Not yet submitted at No time