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Dr Mahdi Rezaei

Dr Mahdi Rezaei

  • Position: University Academic Fellow; Assistant Professor
  • Areas of expertise: Computer Vision; Machine Learning; Autonomous Driving; Self-driving Cars; Road / Traffic Perception; Driver Behaviour Monitoring; Human Factors.
  • Email: M.Rezaei@leeds.ac.uk
  • Phone: +44(0)113 343 5342
  • Location: Office 2.11, Institute for Transport Studies, 34-40 University Road
  • Website: YouTube | Twitter | LinkedIn | Googlescholar | Researchgate | ORCID

Profile

Biography

Dr Mahdi Rezaei is an Assistant Professor in Computer Science and University Academic Fellow (UAF) at the Institute for Transport Studies. He has 15 years of experience in academia and industry and his primary area of expertise and research interests are in Computer Vision, Machine Learning, and Deep Learning. His research mainly focuses on real-world applications in Autonomous Driving and Smart cars, Driver Behaviour Monitoring, Road/Traffic perception, Object Detection and Tracking, Human Factors and Safety. He has published more than 50 journal articles and conference papers, a monograph book with Springer, and 12 book chapters in the field.

He received his PhD degree in Computer Science with the Best PhD Thesis Award, from the University of Auckland, New Zealand (Top 1% world university, QS rank:50), and had the privilege of working with Professor Reinhard Klette in joint collaboration with Daimler (Mercedes Benz). 

PhD Position with Scholarship – If you are passionate & talented in Computer Vision, Deep Learning, and Autonomous Vehicles, I have a competition based funded PhD position available. More information here. 

Academic Employment History:

  • 2020 - Present, Assistant Professor (University Academic Fellow), Uni of Leeds, UK
  • 2017 - 2020, Senior Researcher, Auckland University of Technology, New Zealand
  • 2015 - 2020, Lecturer in Department of Computer Engineering, Qazvin University, IR
  • 2010 - 2017, Honorary Academic Staff, University of Auckland, New Zealand
  • 2006 - 2010, Lecturer in Department of Computer Engineering, Qazvin University, IR

Selected videos & research outputs:

3D Vehicle / Pedestrian detection & Speed Estimation using Deep Neural Networks:

<iframe width="560" height="315" src="https://www.youtube.com/embed/8kPY1fQhZtk" frameborder="-1" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

Vehicles & Pedestrian Classification, Localisation, Speed Detection, and Tracking

Driver Behaviour Monitoring (Distraction, Drowsiness, Phoning / Texting, Yawning, Head Nodding,...)

<iframe width="560" height="315" src="https://www.youtube.com/embed/K_ZIhwqScpk" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

Driver Behaviour analysis uning a single RGB camera

Stop/Go Decision Making at Roundabouts for Autonomous Vehicles

<iframe width="560" height="315" src="https://www.youtube.com/embed/FizrMquM0VU" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

The research aims for a timely, fast and safe merging manoeuvre by AVs at the roundabout where the human may act unsafely or with delays due to uncertainty, fatigue, or distraction. The solution can lead to reducing traffic jams behind roundabouts, save times/lives, less fuel consumption, and less CO pollutions!

Our contribution towards public health during the COVID-19 Pandemic:

<iframe width="560" height="315" src="https://www.youtube.com/embed/FwCP2ySDshE" frameborder="-1" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

Rezaei, M.; Azarmi, M. (2020). DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic. Applied Sciences. 2020, 10(21), 7514.

 

Join our team at the Institute for Transport Studies, Ranked 12th in the world, as one of the most advanced research centres in the interdisciplinary field of Autonomous Vehicles, Self-driving Cars, Driver Behaviour Monitoring, and Human factors.

 Talented and motivated prospective PhD candidates, Postdocs, and Researchers are welcome to apply and join our multideciplinary team at the Institute for Transport Studies (ITS). 

 

My current PhD & MSc Students:

 

Honours, Award, Grants:

  • Research England - World Class Laboratories Fund 2020-2021. (Co-I, £54,852)
  • Spark $100,000 Qualifiers Award, 2011. Also, the winner of “Spark Ideas Challenge Award” for the project IntelliEye (one of the top 3 projects among all applications from all disciplines)
  • Marsden Grant, 2012. Faculty grant for going to the final round of Marsden competition- the most challenging research grant in New Zealand, by Royal Society of New Zealand.
  • 3 x Best Paper Awards: ( IEEE Conference SensorCOMM (2007) | CVPR paper, University of Auckland (2014) | Computer Analysis of Images and Patterns (2019)
  • 2 x Akira Nakamura Awards and Grant: 2011 and 2014. provided by Akira Nakamura, professor emeritus of Hiroshima University. (PI, $60,000)
  • Best PhD Thesis Award, 2014. the University of Auckland
  • Honorary Academic, 2017-2020. Dept. of Computer Science, Auckland University of Technology. 
  • Honorary Academic, 2014-2016. Dept. of Computer Science, the University of Auckland, Researcher at ‘.enpeda..’ research group

 

Professional Engagements:

Editor and Organiser: 

Reviewer: 

  • IEEE Transactions on Intelligent Transportation Systems
  • IEEE Transactions on Vehicular Technologies
  • International Journal of Applied Science
  • International Journal of Computer and Robotics
  • International Journal of Sensors
  • International Conference on Pattern Recognition
  • Asian Conference on Pattern Recognition
  • International Conference of Computer Analysis of Images and Patterns
  • Pacific-Rim Symposium on Image and Video Technology 

Panel Member / Technical Program Committee (TPC): 

  • International Conference of Computer Analysis of Images and Patterns
  • Pacific-Rim Symposium on Image and Video Technology
  • RoboCup Asia-Pacific Symposium
  • IEEE

Latest Publications:

https://scholar.google.co.uk/citations?user=9Z7J8ecAAAAJ&hl=en

 

Responsibilities

  • Research, lecturing, and supervsion of postdocs, PhDs, and postgrad students

Research interests

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning
  • Autonomous Vehicles | Driver Behaviour Monitoring | Pedestrian Detection and Tracking
  • Traffic Science Segmentation and Understanding
  • Human Factors

A short video spotlight on my monograph book published by Springer International Publishing:

Computer Vision for Driver Assistance 

<iframe width="560" height="315" src="https://www.youtube.com/embed/2H9JH9ZBQMI?autoplay=0" frameborder="1" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

Rezaei, M., Klette, R. (2017). Computer Vision for Driver Assistance: Simultaneous Traffic and Driver Monitoring. Springer. CIVI Book Series, pp. 1-224, Volume 45, 2017, ISBN : 978-3-319-50549-7

<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://environment.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>

Qualifications

  • PhD in Computer Science - Computer Vision
  • MSc in Computer Engineering - Artificial Intelligence & Robotics
  • BSc in Computer Engineering- Computer Hardware

Professional memberships

  • IEEE
  • Computer Vision Foundation
  • CerV Research Group

Student education

For your questions and any further information feel free to email me via m.rezaei@leeds.ac.uk 

Human Drive

Human Drive- Autonomous Vehicle

 

Driving Simulaor

Our Sophisticated Driving Simulator

 

University of Leeds

Modern Infrastructures

 

Academic & Social Profiles (click on icons below):

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Research groups and institutes

  • Human Factors and Safety

Current postgraduate researchers

<h4>Postgraduate research opportunities</h4> <p>We welcome enquiries from motivated and qualified applicants from all around the world who are interested in PhD study. Our <a href="https://phd.leeds.ac.uk">research opportunities</a> allow you to search for projects and scholarships.</p>