Established in Cookstown in 2012, Metro Surveillance Group Limited (Metro) specialises in CCTV design, installation, maintenance and monitoring across the UK and Ireland, with a focus on the petroleum station sector. Each of these fuel stations are monitored by CCTV cameras, primarily looking at two key aspects. The first is fulfilling government-driven requirements such as verifying that the correct (legal) fuel is being purchased, and that it is being purchased by an adult. The second is monitoring theft, which often includes vehicles with changed or modified number plates, or reoffending suspects. Currently, this is a very labour intensive process where humans in a control room watch several cameras simultaneously, initiating fuel pumps for use and identify potential theft.
The aim of this project is to automate this process via both vehicle and person identification and re-identification using the CCTV data currently available from fuel stations. Person and vehicle identification and re-identification have recently become major topics in the fields of computer vision and image processing and an essential component in the video surveillance system. Surveillance systems are no longer just one camera, but multi-camera systems that require significant effort to match the appearance of objects across a number of non-overlapping camera views at a range of angles.
This is a challenging problem due to occlusions, background activity, viewpoint change, weather conditions etc. Many approaches to re-identification are based on the use of images (rather than video) and focus on extracting discriminative features or metric learning. However, using only static image data is limiting due to visual ambiguity as well as the lack of spatio-temporal data. Despite recent remarkable progress, re-identification methods still suffer from weak feature representation and the ability to accuracy identify the salient object. To mitigate these issues, we build on our recent work using patch selection techniques and deep learning techniques. Many approaches focus only on person re-ID, whereas we aim to extend these to accurate person and vehicle re-ID.
The project will have two main aspects. Firstly, we will use deep networks such as a Deep Decompositional Network (DNN) to parse CCTV images into semantic regions, combined with a range of colour matching and background removal techniques. Then, building on the ongoing research of Coleman and Kerr we will develop novel approaches for spatio-temporal feature extraction algorithms for use with video data. Such techniques will underpin new approach of spatio-temporal saliency detection in video surveillance. Initially benchmarking performance evaluation will be conducted using the well-known Market- 1501, DukeMTMC-reID and CUHK03 datasets prior to real-world evaluation using MetroCCTV’s testbeds (such data are a valuable contribution to the project).
The successful candidate will have the opportunity to work at the cutting edge of video surveillance algorithmic development alongside a well-established CCTV company. Additionally, development of these novel video surveillance algorithms will result in high quality conference and journal papers. Opportunities will be provided to attend and present the work at IEEE international conferences and also attend trade shows to understand the competitive nature of this research domain.
- To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
- Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
- A demonstrable interest in the research area associated with the studentship
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
- First Class Honours (1st) Degree
- Masters at 70%
This project is funded by: DFE CAST award in collaboration with MetroCCTV
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 16,500 (tbc) per annum for three years (subject to satisfactory academic performance). EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK.
The Doctoral College at Ulster University
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
I started my PhD at Ulster University after I received the master degree in computer application and technology from the School of Mathematics and Computer Science, Fujian Normal University, China. My research interests are feature extraction, face verification and pattern recognition.The proudest moments of my PhD when my papers were accepted by journals and I received my PhD certificate. It is a long journey to pursue a PhD, I couldn't have got through this without the constant support, help and encouragement from my supervisors and friends. Many thanks all of them.
Huan Wan - PhD in Computer Science and Informatics
I received the bachelor’s of engineering degree in computer science and technology from Shangrao Normal University, Jiangxi, China, in 2013; and the master’s degree in computer application and technology from the School of Mathematics and Computer Science, Fujian Normal University, China. When I was pursuing a PhD degree at Ulster University, I continued my research on face recognition and image representation.This long journey has only been possible due to the constant support and encouragement of my first supervisor. I also like to thank my second supervisor for his patience, support and guidance during my research studies. My favourite memory was the days of exercising, gathering and playing with my friends here. If I could speak to myself at the start of my PhD, the best piece of advice I would give myself would be "submit more papers to Journals instead of conferences".
Xin Wei - PhD in Computer Science and Informatics
After master’s degree, I joined the Artificial Intelligence Research Group in the School of Computing at Ulster University to pursue my PhD. I would like to thank my supervisors for their guidance, invaluable advice, encouragement and support throughout my PhD.My proudest moments were when my research papers were accepted in prestigious conferences and journals. I feel accomplished about the six first-author publications from my doctoral research. Also, I have had the honour of receiving the Best Student Paper Award at the 2018 International FLINS Conference.I love travelling; my favourite memories were travelling to present my research in addition to getting the opportunity to meet leading researchers from different parts of the world. And I couldn't have achieved this without the support of my friends and family.
Niloofer Shanavas - PhD in Computer Sciences and Informatics
In the whole PhD ordeal, my supervisory team played a tremendous role:- they are three in a million. They are perfect supervisors who perfectly know which milestones or pathways to be taken during research initiatives, and they understand the roles of virtually all stages in the journey of PhD. They showcased superior abilities in managing and motivating me evoking high standards; demonstrating a commitment to excellence. Jane and Haiying guided me as their daughter and Fiona turned out to be the best of friends.I heard from “Eleanor Roosevelt” that “The future belongs to those who believe in the beauty of their dreams.” The dream with which I grew up to become a Doctor one day, has finally come true. In the journey of PhD, I embraced that a PhD is not just the highest degree in Education but rather it is a life experience where perseverance is the key. I can never forget words from my external examiner Prof Yike Guo, from Imperial College London. His words
Jyotsna Talreja Wassan - PhD in Computer Science and Informatics