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Object classification from mobile and static sensor feeds

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

The demand for video processing is rapidly increasing, driven by greater numbers of sensors with greater resolution, new types of sensors, new collection methods and an ever wider range of applications. For example, video surveillance, vehicle automation or wildlife monitoring, with data gathered in visual/infra-red spectra or SONAR, from multiple sensors being fixed or vehicle/drone-mounted.

This project will focus on a specific application – object (animal) extraction and classification from extremely high-resolution aerial video from moving platforms. Issues of data size, dynamic backgrounds, rapid platform and target movement and classification errors will all need to be resolved and propagated into the final goal – inferring the densities of target species.

The project will require solving substantive computational bottlenecks and creative programming e.g. GPU and distributed file systems. Elements can be found in Erichson & Donovan (2016), but is only a tiny fraction of what is required.

Funding Notes

Multiple sources of scholarship funding are potentially available, including university, research council (EPSRC) and research group (CREEM). Some are open to international students, some to EU and some UK only.

Applicants should have a good first degree in mathematics, statistics or another discipline with substantial numerical component. Applicants with degrees in other subjects (e.g., biology) should have the equivalent of A-level/Higher mathematics, and experience using statistical methods; such candidates should discuss their qualifications with the Postgraduate Officer. A masters-level degree is an advantage.

Further details of the application procedure, including contact details for the Postgraduate Officer, are available at View Website

References

Erichson, N. B. & Donovan, C. R. (2016) Randomized low-rank Dynamic Mode Decomposition for motion detection. Computer Vision and Image Understanding. Vol. 146 pp 40-50.

How good is research at University of St Andrews in Mathematical Sciences?

FTE Category A staff submitted: 30.60

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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