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Interpretation of animal behaviors using novel video processing techniques in Artificial Intelligence

   Department of Electronic and Electrical Engineering

About the Project

This PhD project will introduce new video processing and analysis tools based on state-of-the-art innovations in Artificial Intelligence (AI). Specifically, this research will aim to make contributions in the area of Deep Learning with the goal of designing novel algorithms for analysing video data for application in the agri-tech sector. The aim is to develop new capabilities to interpret animal behaviours in order to monitor the health and wellbeing of cows, pigs and other livestock.

 Dairy production is a highly complex process made particularly challenging by the shortage of highly skilled labour and the need for proactive management approaches, all set against a background of downward pressure on milk prices. Both in the UK and internationally there has been a continuous trend over the last two decades towards increased dairy farm sizes, as this provides opportunities for improved production efficiencies. This brings additional challenges including increased scrutiny of Animal Welfare and of Environmental Impact, especially the carbon footprint of milk production.

 The increasing power of artificial intelligence now makes it possible for expertise and observation to be provided autonomously through the use of electronic monitoring and automated analysis. in this project, we aim to introduce new deep learning architectures which can be used specifically for detecting, recognising, tracking and analysing the behaviour of individual animals in a barn using video from existing cameras. The techniques developed will initially be applied to provide a fast, effective, automatic approach for monitoring animal welfare and detecting the early onset of disease, illness or other medical complications in animals through recognition of anomalous behaviours over time. We will begin by designing techniques for monitoring behaviour of cows before extending the same techniques to work with barn videos of pigs.

 Candidates with a strong background in image and signal processing and/or machine learning are sought. Experience of one or more programming languages and/or statistical analysis packages would be an advantage.

 This is an exciting opportunity for the successful candidate to join an internationally recognised research team based at the Centre for Signal & Image Processing (CeSIP) within the Department of Electronic and Electrical Engineering at the University of Strathclyde. The Department is host to a large number of researchers and PhD students, many of whom are based in the £89M Technology Innovation Centre and working directly with our industrial partners to deliver useful, high-impact research. The successful candidate will engage with the wider research team on a daily basis and will also have opportunities to interact with and present their research to our industrial partners and the wider research community. There will be opportunities for the successful candidate to attend one international conference during their studies and to interact with the supervisory team’s academic collaborators who are based at various universities around the world.

 This project is supported both financially and in-kind by Peacock Technology who will provide funding, data, hardware and much more in the way of in-kind contributions to ensure the success of this project.

 Peacock Technology has been a Scottish Engineering consultancy for over 10 years and has a proven track record in Agri-Tech having delivered projects including robotics for vaccination of live fish, robotics for DNA sampling of beef carcasses, vision systems for biomass estimation of pigs and fish, and many others. Peacock employs 25 people and has a highly qualified Engineering team including engineers with PhDs in Software Engineering, Mechanical Engineering and Mathematics. Peacock is committed to the Engineering profession and is an IET Enterprise Partner, and many members of the Peacock team are accredited to IEng or CEng.

 Ultimately, this PhD position presents a unique opportunity for the successful candidate to design and deliver innovate solutions in direct collaboration with industry. There are many opportunities for publication in peer reviewed journals and conferences as well as delivering real industrial impact through research. 

Funding Notes

This PhD position is supported by a generous studentship which provides funding to cover all HOME fees (UK & eligible – see below - EU Students only) and a standard annual EPRSC stipend in the region of £16k per annum. Funding will also be provided to support travel to at least 1 international conference and all necessary equipment to conduct the PhD will be provided.


The project is funded by EPSRC, Peacock Technology, National Manufacturing Institute Scotland (NMIS) and the University of Strathclyde. Therefore, the applicant should meet the EPSRC studentship eligibility criteria (https://www.ukri.org/councils/esrc/career-and-skills-development/funding-for-postgraduate-training/eligibility-for-studentship-funding/):
• possess a First or Upper second (2.1) class UK BEng Honours or MEng degree in relevant* engineering or physics related subject
• be a UK or an eligible EU national and adhere to EPSRC eligibility criteria
Candidates with the Knowledge and experience of the following are desirable:
• Have experience or a keen interest in one or more of the following: Image Processing/Signal Processing/Machine Learning & AI or Data Analysis
• Have skills and understanding of Matlab/Python (or suitable alternative) and its use in image analysis
• Be a UK/EU national and adhere to Research Council (RCUK) eligibility criteria
*Subjects that would be considered for the position: Electronic & Electrical Engineering, Computer & Electronics Systems, Electrical & Mechanical Engineering, Computer & Information Sciences, Mathematics, Physics
Study modes eligibility: Full time

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