A Unified framework for early anomalies detection

   School of Science, Engineering and Environment

  Dr Sadaqat ur Rehman  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Information on this PhD research area can be found further down this page under the details about the Widening Participation Scholarship given immediately below.

Applications for this PhD research are welcomed from anyone worldwide but there is an opportunity for UK candidates (or eligible for UK fees) to apply for a widening participation scholarship.

Widening Participation Scholarship: Any UK candidates (or eligible for UK fees) is invited to apply. Our scholarships seek to increase participation from groups currently under-represented within research. A priority will be given to students that meet the widening participation criteria and to graduates of the University of Salford. For more information about widening participation, follow this link: https://www.salford.ac.uk/postgraduate-research/fees. [Scroll down the page until you reach the heading “PhD widening participation scholarships”.] Please note: we accept applications all year but the deadline for applying for the widening participation scholarships in 2024 is 28th March 2024. All candidates who wish to apply for the MPhil or PhD widening participation scholarship will first need to apply for and be accepted onto a research degree programme. As long as you have submitted your completed application for September/October 2024 intake by 28 February 2024 and you qualify for UK fees, you will be sent a very short scholarship application. This form must be returned by 28 March 2024. Applications received after this date must either wait until the next round or opt for the self-funded PhD route.


Project description: Civilized nations compete to provide the safest possible settings for their populations. For the sake of citizen security, most modern metropolitan areas have installed extensive networks of surveillance cameras in public parks, streets, airports, and railway-stations. 

The main goal of this PhD project is to develop a real-time solution based on cutting-edge Computer Vision and Deep Learning algorithms that can be deployed in CCTV infrastructure and monitoring grid to form a unified framework capable of detecting and identifying hazards like abnormal social behaviours, car drifting, fire etc. 

The specific goals of the PhD project are: 

  • Mathematical modelling of a light-weight and secure model for different anomalies detection.
  • Development of novel architecture and a unified frame work for detection and recognition of different anomalies for security purpose in real-time.
  • Final implementation of the unified frame work in real-time scenario with less latency.
Computer Science (8)

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