Funded PhD Opportunity

Computer vision for advertising analytics

This project is funded by: Digital Natives

Subject: Computer Science and Informatics


Summary

Outdoor impressions have traditionally been measured using traffic counts and daily effective circulation in order to estimate reach. For years, these figures have been the only reliable counts available to measure the reach of out-of-home (OOH) advertising. More recently outdoor impressions take the focus from the number of consumers that can see an advertisement and convert that figure to a more realistic one estimating the number of consumers who actually noticed the advertisement. Often this requires the use of eye-tracking technology and despite the technological advances in making eye-tracking glasses much smaller and lightweight, their use still poses a possible liability in driving situations that some internal review boards are unlikely to approve.

These impression figures (often in weekly increments) are derived from a variety of data, specifically: Daily Effective Circulations (DECs), Census Data, Travel Surveys, Data Modeling, Analytics, and statistical conversion factors, or Visibility Adjustment Indices (VAIs).  VAIs take the physical characteristics into consideration when dealing with an outdoor structure. The index is based on eye-tracking related research and visibility research to form the statistical basis of an VAI model. This model takes various factors into account to arrive at the VAI of each inventory. These factors include the placement of the unit, what side of the road it’s on when viewed, the distance from traffic when viewed, visibility zone, the type of road it’s placed on, whether the unit is illuminated, the size, its angle relative to oncoming traffic and who it’s viewed by (vehicular traffic, pedestrian traffic, or both).  Other considerations include whether the sign is digital, scrolling or static and the size of each. This project will research and develop computational computer vision approaches to outdoor impression measurement through the use of automated sign recognition technology, saliency detection and demographic data.


Essential criteria

  • Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)
  • Experience using research methods or other approaches relevant to the subject domain

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • Masters at 70%

Funding

This project is funded by: Digital Natives

The scholarships will cover tuition fees and a maintenance award of £15,009 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.


Other information


The Doctoral College at Ulster University


Reviews

As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

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Key dates

Submission deadline
Friday 29 November 2019

Interview Date
16 December 2019


Applying

Apply Online


Campus

Magee campus

Magee campus
A key player in the economy of the north west


Contact supervisor

Dr Dermot Kerr


Other supervisors

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