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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

Dr Mahdi Rezaei is an Assistant Professor of Computer Science and a University Academic Fellow at the Institute for Transport Studies (ITS), ‎University of Leeds. His background and main research interests are AI, ML, and Computer Vision for Autonomous Vehicles including driver/occupant monitoring, pedestrians’ intention prediction, and AV/road-user interactions. He has over 15 years of experience in ‎academia and industry and currently leads a team of computer vision experts and researchers in transportation at the University of Leeds. Dr Rezaei is an executive member of the “Universities Transport ‎Study Group” (UTSG) in the UK, a team leader in the EU Hi-Drive flagship project, a member of the Academic Advisory Group in the LIDA data scientist development programme (LIPAG), and also principle investigator and Co-I of multiple EU and UK funded projects in AV field.

Education

PhD in Computer Science, The University of Auckland. 2014. Top PhD Thesis Award.

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 the Department of Computer Engineering, Qazvin University, IR
  • 2010 - 2017, Honorary Academic Staff, University of Auckland, New Zealand
  • 2006 - 2010, Lecturer in the Department of Computer Engineering, Qazvin University, IR

Honours, Awards, Grants:

  • MaVis – EPSRC, AI Visualisation 2023-2026 –  (COI, £ 713,438.00) 
  • EPSRC Impact Acceleration Account (IAA) 2022-2023 –  (PI, £287,295.00)
  • Hi-Drive – EU funded 2021-2025 –  (COI,  £ 1,508,785.00) 
  • Converted UDRIVE Dataset –  DLR, German Aerospace Centre –  (PI, £6,000.00) 
  • L3Pilot – EU Horizon 2020 Driving Automation 2017-2021 – (COI, € 877,500.00)
  • Research England - World Class Laboratories Fund 2020-2021  – (COI, £54,852.00)
  • 2 x Akira Nakamura Awards and Grant: 2011 and 2014. provided by Akira Nakamura, professor emeritus of Hiroshima University. (PI,  ¥ 60,000.00)
  • New Zealand Spark Ideas Challenge, $100,000.00 Qualifiers Award, 2011.
  • Marsden Grant, 2012. Faculty grant for going to the final round of Marsden competition- the most challenging research grant in New Zealand, by the 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)
  • Best PhD Thesis Award, 2014. the University of Auckland

Selected videos & research outputs:

3D Vehicle Detection / Pedestrian Detection, and Speed Estimation Using a Single Camera:

<iframe width="560" height="315" src="https://www.youtube.com/embed/FdiQ_EGbZe0" frameborder="-1" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

Traffic Monitoring including 3D Vehicles & Pedestrian Detection, Speed Detection & Tracking and activity heatmap generation.

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 using 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, saving times/lives, less fuel consumption, and less CO pollution!

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 prospective PhD students with relevant background are welcome to apply via our online system here and join our multideciplinary team at Institute for Transport Studies (ITS)*.

* Funded positions will be advertised publicly.

 

My Postdocs & PhD Students:

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>

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>