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Faisal Qureshi, PhD - University of Ontario Institute of Technology. Oshawa, ON, CA

Faisal Qureshi, PhD Faisal Qureshi, PhD

Associate Professor and Undergraduate Program Director, Computer Science, Faculty of Science | University of Ontario Institute of Technology

Oshawa, ON, CANADA

Innovating intelligent traffic control systems to ease congestion and reduce air pollution





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A long daily commute for millions of drivers fuels serious health and economic consequences. Vehicle emissions are the leading cause of air pollution in North America; and idling for long periods in traffic each day significantly reduces productivity. In an effort to curb traffic congestion in urban centres, Faisal Qureshi, PhD, Associate Professor of Computer Science, in the Faculty of Science, is developing new techniques to capture and analyze big data involved in traffic camera networks, to better understand traffic patterns and conditions. Using that data, he is developing sensors for next generation smart camera networks to detect incidents, divert vehicles and improve traffic flow in urban centres and on highways. This emerging technology will make more intelligent use of existing road networks, help reduce air pollution, and ultimately mitigate climate change.

His research also has meaningful benefits for older adults who want to live independently at home for as long as possible. Dr. Qureshi is creating smart camera networks to allow family members to monitor elderly relatives, particularly those with dementia. To facilitate smart homes, he is developing sensors that family members could program to give cues to the user, reminding them to complete important daily tasks, and helping them maintain independence.

A computer science expert, Dr. Qureshi is fascinated by understanding how human intelligence works and using it to build novel systems capable of carrying out complex tasks without human intervention. He earned his Bachelor of Science in Mathematics with a Minor in Physics from Punjab University in Lahore, Pakistan in 1992, and his Master of Science in Electronics from Quaid-e-Azam University in Islamabad, Pakistan in 1995.

Dr. Qureshi joined UOIT in July 2008 as an assistant professor. Instrumental in establishing the university’s Visual Computing (VC) Lab, he was named associated professor in July 2013. He gained industry experience as a software engineer with Toronto-based Autodesk Canada Co., and as a contract engineer with MDRobotics in Brampton, Ontario.

Industry Expertise (5)




Computer Gaming

Computer/Network Security

Areas of Expertise (9)

Computer Vision

Camera Networks

Computer Graphics

Smart Graphics

Behaviour-based Computer Animation

Cognitive Vision

Video Surveillance

Autonomous Characters for Computer Animation and Games

Autonomous Agent Architectures

Accomplishments (2)

Director, Visual Computing (VC) Lab (professional)

Dr. Qureshi established UOIT's state-of-the-art VC Lab which focuses on research problems that reside at the intersection of computer vision, visual sensor networks, and computer graphics.

Co-Chair, 13th Conference on Computer and Robot Vision (professional)


Co-Chair of the 12th Conference on Computer and Robot Vision in Halifax, Nova Scotia in June 2015, Dr. Qureshi will also Co-Chair next year's conference in Victoria British Columbia in June 2016.

Education (4)

University of Toronto: PhD, Computer Science 2007

University of Toronto: MSc, Computer Science 2000

Quaid-e-Azam University: MSc, Electronics 1995

Punjab University: BSc, Mathematics & Physics (Minor) 1992

Affiliations (3)

  • Institute of Electrical and Electronics Engineers
  • Association for Computing Machinery
  • Canadian Image Processing and Pattern Recognition Society

Event Appearances (6)

Towards Efficient Feedback Control in Streaming Computer Vision Pipelines

Workshop on User-Centred Computer Vision  Singapore


Accelerating Cost Volume Filtering Using Salient Subvolumes and Robust Occlusion Handling

12th Asian Conference on Computer Vision (ACCV 2014)  Singapore


A Stream Algebra for Computer Vision Pipelines

Second Workshop on Web-scale Vision  Columbus, Ohio


Topic Models for Image Localization

Tenth Conference on Computer and Robot Vision (CRV 2013)  Regina, Saskatchewan


I Remember Seeing This Video: Image Driven Search in Video Collections

Tenth Conference on Computer and Robot Vision  Regina, Saskatchewan


Droplet Tracking from Unsynchronized Cameras

2nd International Conference on Pattern Recognition Applications and Methods (ICPRAM 2013)  Barcelona, Spain


Research Grants (3)

Intelligent Traffic Control Through Multimodal Vehicle Detection and Classification

Ontario Centres of Excellence Voucher for Innovation and Productivity $43000


This one-year research project focuses on the development of a multimodal vehicular traffic detection and analysis system.

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Next Generation Smart Camera Networks

NSERC Discovery Grant $90000


This research aims to develop new techniques to handle big data involved in large scale systems such as traffic camera networks, to identify individuals and detect incidents, and store them for later use.

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Camera-based Vehicle Detection and Classification

NSERC Engage Grant $63078


This research examines current camera-based vehicle detection and classification systems, with the goal of developing a multimodal vehicular traffic detection and analysis system.

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Courses (1)

Principles of Computer Science

2nd Year, Undergraduate Course

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Articles (3)

Stereo Reconstruction of Droplet Flight Trajectories IEEE Transactions on Pattern Analysis and Machine Intelligence


This article presents the development of a new method for extracting 3D flight trajectories of droplets using high-speed stereo capture. Results suggest that, even when full stereo information is available, unsynchronized reconstruction using the global motion model can significantly improve the 3D estimation accuracy.

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Software Laboratory for Camera Networks Research IEEE Journal on Emerging and Selected Topics in Circuits and Systems


This research presents a distributed virtual vision simulator capable of simulating large-scale camera networks, and pedestrian traffic in different 3D environments. Specifically, this research shows that the proposed virtual vision simulator can model a camera network, comprising more than one hundred active pan/tilt/zoom and passive wide field-of-view cameras, deployed in an upper floor of an office tower in downtown Toronto.

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Automatic Parsing of Lane and Road Boundaries in Challenging Traffic Scenes Spie Journal of Electronic Imaging


Automatic detection of road boundaries in traffic surveillance imagery can greatly aid subsequent traffic analysis tasks, such as vehicle flow, erratic driving, and stranded vehicles. This paper develops an online technique for identifying the dominant road boundary in video sequences captured by traffic cameras under challenging environmental and lighting conditions, e.g., unlit highways captured at night. The proposed method works in real time of up to 20  frames/s and generates a ranked list of road regions that identify road and lane boundaries. Results show that this method outperforms two state-of-the-art techniques in precision, recall, and runtime.

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