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Dr Mahdi Rezaei

Dr Mahdi Rezaei

  • Position: Associate Professor of Computer Vision & Machine Learning
  • Areas of expertise: Computer Vision | AI & Machine Learning | Autonomous Vehicles | Multimodal Large Language Models (MLLMs) | Driver Monitoring Systems | Pedestrian Behaviour Analysis | Road Users Safety | Micromobility
  • 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 | LinkedIn | Googlescholar | ORCID

Profile

Dr. Mahdi Rezaei is an Associate Professor of computer vision and machine learning 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 monitoring systems (DMS), Connected, Cooperative, and Automated Mobility (CCAM)‎; Pedestrian intention prediction, and AV-road user interactions. He has over 18 years of experience in ‎academia and industry, and currently leads a team of computer vision experts and researchers in transportation and intelligent vehicles 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 program (LIPAG). He is also the Principal Investigator / Co-investigator in more than £3.4 million funded projects in international consortiums, Horizon Europe, EPSRC, and UKRI calls.

Education

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

Employment History:

  • 2023 - Present, Associate Professor, University of Leeds, UK
  • 2020 - 2023, Assistant Professor (University Academic Fellow), University 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.
  • 4 x Best Paper Awards: ICPR 2024, CAIP 2019, CVPR 2014 by University of Auckland, SensorCOMM 2007.
  • 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:

Traffic Monitoring including 3D Vehicles and pedestrian Detection, Speed Detection and tracking and activity heatmap generation.

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

Driver Behaviour analysis using a single RGB camera

Stop/Go Decision Making at Roundabouts for Autonomous Vehicles

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:

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.

 

Current and Past Postdocs & PhD Students:

Professional Engagements:

Editorial: 

Reviewer: 

  • IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR)
  • IEEE Transactions on Intelligent Transportation Systems (T-ITS)
  • IEEE Transactions on Vehicular Technologies (T-VT)
  • 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 

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

Latest Publications:

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

 

Responsibilities

  • Leader of the Computer Vision Group

Research interests

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

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

Computer Vision for Driver Assistance 

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>Some research projects I'm currently working on, or have worked 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
  • MSc in Artificial Intelligence and Robotics
  • BENg Computer Engineering

Professional memberships

  • Intelligent Transportation Systems Society
  • LIDA
  • IEEE
  • Computer Vision Foundation
  • CerV Research Group
  • Universities' Transport Study Group (UTSG)

Student education

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

Human Drive

Human Drive- Automated Vehicle

 

Driving Simulaor

University of Leeds Driving Simulator (best of its kind in academia).

 

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>
Projects
    <li><a href="//phd.leeds.ac.uk/project/2263-epsrc-dla:-multimodal-large-language-models-and-data-fusion-for-driver-monitoring-in-automated-vehicles">EPSRC DLA: Multimodal Large Language Models and Data Fusion for Driver Monitoring in Automated Vehicles</a></li>