Research course

Computer Vision and Deep Learning based AR/VR and Metaverse in Air Transport

Institution
University of Salford · School of Science, Engineering and Environment
Qualifications
PhD

Entry requirements

Please use this Research Proposal, Personal statement and CV: GUIDE when preparing an application.

Months of entry

Anytime

Course content

Virtual representation of an airport within the metaverse, accessible through augmented reality (AR) and virtual reality (VR) technologies, allows passengers to navigate, access information, and interact with the airport environment digitally, often providing features like virtual check-in counters, wayfinding assistance, and even virtual shopping experiences, all while being overlaid on their real-world view through AR or fully immersed in a virtual environment through VR.

To make the virtual tour effective and realistic, the role of Image Processing (IP), Computer Vision (CV) and Deep Learning (DL) are important. For example, the combination of IP, CV, and DL can assist the user in finding an exact item in the shop without confusion, which saves lots of time. Similarly, the check-in counter of a particular airline and gates can be identified. Therefore, this study explores the concepts of inter-disciplinaries to develop a metaverse system that impacts live and real-time applications significantly.

Enhanced Navigation: Passengers can use AR overlays to see directions, gate locations, and other important information directly on their view of the real airport, making it easier to navigate, especially for first-time travellers. Passengers can use enhanced navigation to explore departure, transit, and destination airports. Virtual assistants can guide passengers through the destination airport and to their hotel/accommodation. The combination of IP, CV DL can be used to identify the first-time travellers automatically to recommend this virtual tour and guide and familiarise them with the airport environment based on the behaviour of the person.

Virtual Exploration: VR can allow users to virtually explore the airport layout before arriving, familiarizing themselves with amenities and locations. In this case, the GPS information can be explored to locate the exact location through artificial intelligence methods.

Personalized Information: Through AR or VR interfaces, passengers can access tailored flight information, wait times, and personalized recommendations for shops and restaurants based on their preferences, as well as receive instructions in real-time. To receive accurate information, the study can explore artificial intelligence techniques to filter out unwanted information which might be big data.

Accessibility Features: VR can be used to create accessible experiences for passengers with disabilities, allowing them to virtually navigate the airport and understand the layout before arriving. The IP, CV and DL are useful in identifying the disability irrespective of gender, age and external adverse factors.

Potential applications of AR VR along with AI in airports include, but are not limited to: check-in and boarding (virtual check-in counters, boarding pass scanning through AR); security screening (visual instructions on how to prepare for security checks through AR); retail experiences (virtual product try-ons, interactive shopping displays using AR); wayfinding assistance: real-time navigation with AR arrows and directions; airport information access (accessing flight updates, gate changes, and delays directly through AR overlays).

Metaverse with AI allows to make air travel more enjoyable and less stressful. This is especially important for elderly passengers and passengers with disabilities (both physical and hidden).

The aim of this research is to develop an airport metaverse through Artificial Intelligence techniques which include IP, CV and DL to provide support to specific categories of passengers, including passengers with physical and hidden disabilities.

Fees and funding

This programme is self-funded.

To enquire about University of Salford funding schemes – including the Widening Participation Scholarship – visit this website.

Qualification, course duration and attendance options

  • PhD
    full time
    36 months
    • Campus-based learningis available for this qualification
    part time
    60 months
    • Campus-based learningis available for this qualification

Course contact details

Name
SEE PGR Support
Email
PGR-SupportSSEE@salford.ac.uk