Moving Towards End-to-End Multi-Target Tracking for Animal Welfare
Multi-target object tracking is a challenging computer-vision problem with a diverse range of applications ranging from security, to sports-analysis, and animal-welfare. This problem involves recording the position all objects of interest, such as people or animals, at each time-step. This is a very challenging task due to overcrowding, complex appearance changes, long-term occlusions, and the difficulty of accurately detecting deformable objects from different viewpoints and camera angles. However, the implications of accurate multi-target tracking for animal welfare are significant, since it will allow for monitoring the behaviour and wellbeing of animals, either in farms or in the wild, with minimal interference to their behaviour and interactions. The problem of multi-target tracking is still largely unsolved for the general case. Most approaches to date consist of a complex pipeline of different software and multiple stages to detect objects, model their appearance, associate detections into short tracklets using a graph of evidence, and finally associate tracklets into longer trajectories. This software pipeline is not only computationally inefficient, but also propagates errors across the different steps, which makes it hard to improve the overall tracker performance as so many different components must be considered. As an alternative, taking inspiration from other areas of computer-vision, where end-to-end trained deep-learning method have proliferated, the field of multi-target tracking has recently started to move towards learning-based approaches.
Multi-target object tracking is a challenging computer-vision problem with a diverse range of applications ranging from security, to sports-analysis, and animal-welfare. This problem involves recording the position all objects of interest, such as people or animals, at each time-step. This is a very challenging task due to overcrowding, complex appearance changes, long-term occlusions, and the difficulty of accurately detecting deformable objects from different viewpoints and camera angles. However, the implications of accurate multi-target tracking for animal welfare are significant, since it will allow for monitoring the behaviour and wellbeing of animals, either in farms or in the wild, with minimal interference to their behaviour and interactions.
The problem of multi-target tracking is still largely unsolved for the general case. Most approaches to date consist of a complex pipeline of different software and multiple stages to detect objects, model their appearance, associate detections into short tracklets using a graph of evidence, and finally associate tracklets into longer trajectories. This software pipeline is not only computationally inefficient, but also propagates errors across the different steps, which makes it hard to improve the overall tracker performance as so many different components must be considered. As an alternative, taking inspiration from other areas of computer-vision, where end-to-end trained deep-learning method have proliferated, the field of multi-target tracking has recently started to move towards learning-based approaches.
In this PhD project we propose to extend and adapt recent learning-based approaches for multi-target tracking to the animal welfare use-case. This will require adapting and extending existing methods to cope with the unique challenges of this domain. The eventual aim would be to create a complete real-time end-to-end tracking solution that could be deployed with minimal human intervention.
Some problems specific to multi-target tracking for the animal welfare domain will be considered, including:
-The appearance of animals may be very similar, meaning that other sources of evidence, such as motion of individuals or very fine-grained appearance differences need to be modelled.
-Overcrowded scenes that exhibit heterogeneous motion, such as can be observed in many farm environments.
To deal with these issues, we would explore regression-based tracking approaches [1,2], that can integrate low-level pixel information from multiple frames in order to produce precise tracklets in the presence of detector inaccuracy, as well as deep graph networks [3-7], which combine spatial and temporal information to predict the association of objects.
 Learning a Neural Solver for Multiple Object Tracking
 Tracking without bells and whistles
 Joint Object Detection and Multi-Object Tracking with Graph Neural Networks
 Graph Neural Networks for 3D Multi-Object Tracking
 Graph Convolutional Tracking
 Joint Object Detection and Multi-Object Tracking with Graph Neural Networks
 Deep Association: End-to-end Graph-Based Learning for Multiple Object Tracking with Conv-Graph Neural Network
-Build a regression-based tracker that combines appearance and motion information across frames in video to create accurate low-level tracklets
-Investigate use of deep graph neural networks for data association in tracking.
-Investigate how prior knowledge of animal motion models can be taken into account
-Explore how attention mechanisms can be used to focus deep neural network’s attention on relevant evidence while ignoring distractors during tracking
-Integrate regression-tracker with graph-network-based data-association so that object detection and appearance models networks can be trained end-to-end with data association
-Evaluate the end-to-end deep learning-based tracking approach for animal welfare applications. Specifically, cows, pigs and chicken farms sequences will be used as relevant scenarios
By completing the objectives, the student will obtain multiple skills (i.e., mathematical and programming skills) as well as strong knowledge in computer vision, machine learning and deep learning.
A minimum 2.1 honours degree or equivalent in Computer Science or Electrical and Electronic Engineering or relevant degree is required.
This 3 year PhD studentship, potentially funded by the Department for Employment and Learning (DfE), commences on 1 October 2021.
Eligibility for both fees and maintenance (approximately £15,000) depends on the applicants being either an ordinary UK resident or those EU residents who have lived permanently in the UK for the 3 years immediately preceding the start of the studentship. Non UK residents who hold EU residency may also apply but if successful may receive fees only.
Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/
Further information available at: https://www.qub.ac.uk/schools/eeecs/Research/PhDStudy/
Computer Science overview
The School of Electronics, Electrical Engineering and Computer Science (EEECS) aims to enhance the way we use technology in communication, data science, computing systems, cyber security, power electronics, intelligent control, and many related areas.
You’ll be part of a dynamic doctoral research environment and will study alongside students from over 40 countries world wide; we supervise students undertaking research in key areas of computer science, including: computing systems, artificial intelligence and cybersecurity. As part of a lively community of over 100 full-time and part-time research students you’ll have the opportunity to develop your research potential in a vibrant research community that prioritises the cross-fertilisation of ideas and innovation in the advancement of knowledge.
Many PhD studentships attract scholarships and top-up supplements. PhD programmes provide our students with the opportunity to acquire an extensive training in research techniques.
Within the School we have a number of specialist research centres including a Global Research Institute, the Institute of Electronics, Communications and Information Technology (ECIT) specialising in Cyber Security, Wireless Innovation and Data Science and scalable computing.
Computer Science Highlights
- Queen’s researchers have strong links with the local industry, which boasts a rich mix of local startups and multi-nationals. Belfast is the second fastest growing region in the UK in terms of Knowledge Economy activity (Northern Ireland Economy Report, 2018).
World Class Facilities
- The state-of-the-art £14m Computer Science Building and the Institute of Electronics, Communications and Information Technology offer bespoke research environments.
Internationally Renowned Experts
- You will be working under the supervision of leading international academic experts.
Research students are encouraged to play a full and active role in relation to the wide range of research activities undertaken within the School and there are many resources available including:
- A wide range of personal development and specialist training courses offered through the Personal Development Programme
- Access to the Queen's University Postgraduate Researcher Development Programme
- Office accommodation with access to computing facilities and support to attend conferences for full-time PhD students
Research within the School is organised into research themes combining strengths by working together on major projects, in many cases in collaboration with key technology companies.
ECIT brings together internationally recognised research groups specialising in key areas of advanced digital and communications technology.
PhD Opportunities are available in a wide range of computer science subjects, aligned to the specific expertise of our PhD supervisors.
Queen’s is a leader in commercial impact and one of the five highest performing universities in the UK for intellectual property commercialisation. We have created over 80 spin-out companies. Three of these -
Kainos, Andor Technology and Fusion Antibodies - have been publicly listed on the London Stock Exchange.
Queen’s has strong collaborative links with industry in Northern Ireland, and internationally. It has a strong funding track record with EPSRC and the EC H2020 programme.
The research profile produced by the 2014 UK Research Excellence Framework (REF) graded 80 per cent of our research activity as 'world-leading' or 'internationally excellent', confirming the School's reputation as an internationally-leading department.
For further information on career opportunities at PhD level please contact the Faculty of Engineering and Physical Sciences Student Recruitment Team on askEPS@qub.ac.uk. Our advisors - in consultation with the School - will be happy to provide further information on your research area, possible career prospects and your research application.
People teaching you
There is no specific course content as such. You are expected to take research training modules that are supported by the School which focus on quantitative and qualitative research methods. You are also expected to carry out your research under the guidance of your supervisor.
Over the course of study you can attend postgraduate skills training organised by the Graduate School.
You will normally register, in the first instance, as an ‘undifferentiated PhD student’ which means that you have satisfied staff that you are capable of undertaking a research degree. The decision as to whether you should undertake a PhD is delayed until you have completed ‘differentiation’.
Differentiation takes place about 8-9 months after registration for full time students and about 16-18 months for part time students: You are normally asked to submit work to a panel of up two academics and this is followed up with a formal meeting with the ‘Differentiation Panel’. The Panel then make a judgement about your capacity to continue with your study. Sometimes students are advised to revise their research objectives or to consider submitting their work for an MPhil qualification rather than a doctoral qualification.
To complete with a doctoral qualification you will be required to submit a thesis of approx 80,000 words and you will be required to attend a viva voce [oral examination] with an external and internal examiner to defend your thesis.
A PhD programme runs for 3-4 years full-time or 6-8 years part-time. Students can apply for a writing up year should it be required.
The PhD is open to both full and part time candidates and is often a useful preparation for a career within academia or consultancy.
Full time students are often attracted to research degree programmes because they offer an opportunity to pursue in some depth an area of academic interest.
The part time research degree is an exciting option for professionals already working in the education field who are seeking to extend their knowledge on an issue of professional interest. Often part time candidates choose to research an area that is related to their professional responsibilities.
If you meet the Entry Requirements, the next step is to check whether we can supervise research in your chosen area. We only take students to whom we can offer expert research supervision from one of our academic staff. Therefore, your research question needs to engage with the research interests of one of our staff.
- Assessment processes for the Research Degree differ from taught degrees. Students will be expected to present write up their work at regular intervals to their supervisor who will provide written and oral feedback; a formal assessment process takes place annually.
This Annual Progress Review requires students to present their work in writing and orally to a panel of academics from within the School. Successful completion of this process will allow students to register for the next academic year.
The final assessment of the doctoral degree is both oral and written. Students will submit their thesis to an internal and external examining team who will review the written thesis before inviting the student to orally defend their work at a Viva Voce.
- Supervisors will offer feedback on the research work at regular intervals throughout the period of registration on the degree.
Full time PhD students will have access to a shared office space and access to a desk with personal computer and internet access.
The minimum academic requirement for admission to a research degree programme is normally an Upper Second Class Honours degree from a UK or ROI HE provider, or an equivalent qualification acceptable to the University. Further information can be obtained by contacting the School.
For information on international qualification equivalents, please check the specific information for your country.
English Language Requirements
Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component or equivalent qualification acceptable to the University is required (*taken within the last 2 years).
International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.
For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
As a result of the COVID-19 pandemic, we will be offering Academic English and Pre-sessional courses online only from June to September 2020.
|Northern Ireland (NI) 1||£4,500|
|Republic of Ireland (ROI) 2||£4,500|
|England, Scotland or Wales (GB) 1||£4,500|
|EU Other 3||£22,000|
1 EU citizens in the EU Settlement Scheme, with settled or pre-settled status, are expected to be charged the NI or GB tuition fee based on where they are ordinarily resident, however this is provisional and subject to the publication of the Northern Ireland Assembly Student Fees Regulations. Students who are ROI nationals resident in GB are expected to be charged the GB fee, however this is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.
2 It is expected that EU students who are ROI nationals resident in ROI will be eligible for NI tuition fees, in line with the Common Travel Agreement arrangements. The tuition fee set out above is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
All tuition fees quoted are for the academic year 2021-22, and relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
For further information please refer to www.qub.ac.uk/brexit-advice/information-for-students.
Computer Science costs
There are no specific additional course costs associated with this programme.
Additional course costs
Depending on the programme of study, there may also be other extra costs which are not covered by tuition fees, which students will need to consider when planning their studies . Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £100 per year for photocopying, memory sticks and printing charges. Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen. There are also additional charges for graduation ceremonies, and library fines. In undertaking a research project students may incur costs associated with transport and/or materials, and there will also be additional costs for printing and binding the thesis. There may also be individually tailored research project expenses and students should consult directly with the School for further information.
How do I fund my study?1.PhD Opportunities
Find PhD opportunities and funded studentships by subject area.2.Funded Doctoral Training Programmes
We offer numerous opportunities for funded doctoral study in a world-class research environment. Our centres and partnerships, aim to seek out and nurture outstanding postgraduate research students, and provide targeted training and skills development.3.PhD loans
The Government offers doctoral loans of up to £26,445 for PhDs and equivalent postgraduate research programmes for English- or Welsh-resident UK and EU students.4.International Scholarships
Information on Postgraduate Research scholarships for international students.
Funding and Scholarships
The Funding & Scholarship Finder helps prospective and current students find funding to help cover costs towards a whole range of study related expenses.
How to Apply
Find a supervisor
If you're interested in a particular project, we suggest you contact the relevant academic before you apply, to introduce yourself and ask questions.
To find a potential supervisor aligned with your area of interest, or if you are unsure of who to contact, look through the staff profiles linked here.
You might be asked to provide a short outline of your proposal to help us identify potential supervisors.