Imperial College London Featured PhD Programmes
Gdansk University of Technology Featured PhD Programmes
FindA University Ltd Featured PhD Programmes

Autonomous Multi-Camera Monitoring Systems


   Department of Computer Science

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Dr R Calinescu, Dr A Bors  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Research areas: Autonomous and self-adaptive systems; Computer Vision and Image Processing; Automated and Model-Driven Software Engineering; Safety of autonomous and self-adaptive systems; Software engineering


This project is about developing a new methodology for managing intelligent systems of distributed synchronous cameras. Multi-camera systems are increasingly used to identify emerging risks in large buildings and areas where many people walk and interact through
successions of corridors and open spaces. [1,2] Their applications range from monitoring patient well-being in hospitals to tracking antisocial behaviour in retail centres and detecting terrorist activity at airports. Systems of pan-zoom-tilt cameras used in such applications are very complex and notoriously tedious and error-prone to monitor and continually adjust by human security agents. We propose a PhD project that will develop a methodology to automate the evaluation of the activity of individuals and groups using complex autonomous multi-camera monitoring systems. The PhD candidate will develop:

1. Distributed algorithms for monitoring individual and group activities and event detection from multi-camera video sequences. This part of the project will extend existing algorithms for the identification of human activity [3] from single-camera video sequences devised in a previous project led by AB. Multi-camera systems will enable better capabilities such as those provided by 3D modelling of group activities 4 and the tracking of unfolding events through complex networks of cameras. Dynamic modelling on graphs will be used to model changing patterns in movement.

2. Model-driven engineering techniques for the dynamic reconfiguration of camera parameters such as pan-tilt angles and zooming, to improve the scene observation and to track complex events involving multiple individuals. Building on recent research led by RC, [5,6] this project component will use runtime stochastic modelling and verification to continually assess the risk situation and adjust the camera configurations accordingly. This will allow multi-camera systems to follow unfolding events and to react to adverse changes such as a camera being damaged accidentally or maliciously.

References

1 X. Wang, Intelligent multi-camera video surveillance: A review. Pattern recognition letters 34(1):3-19, 2013.

2 L. Bazzani et al., Joint Individual-Group Modeling for Tracking. IEEE Trans Pattern Analysis Mach Intell 37(4):746-759, 2015.

3 K. Stephens, A. G. Bors, Observing human activities using movement modelling, AVSS:44_1-44_6, 2015.

4 M. Grum, A. G. Bors, 3D modeling of multiple-object scenes from sets of images, Pattern Recognition 47:326-343, 2014.

5 R. Calinescu et al. Self-Adaptive Software with Decentralised Control Loops. FASE:235-251, 2015. 6 R. Calinescu et al., Formal Verification with Confidence Intervals to Establish Quality of Service Properties of Software Systems. IEEE Trans Reliability PP:1-19, 2015.

How good is research at University of York in Computer Science and Informatics?


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

Click here to see the results for all UK universities
PhD saved successfully
View saved PhDs