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Details of Grant 

EPSRC Reference: EP/N033779/1
Title: LOCATE: LOcation adaptive Constrained Activity recognition using Transfer learning
Principal Investigator: Damen, Professor D
Other Investigators:
Researcher Co-Investigators:
Project Partners:
QinetiQ Thales Ltd Xerox
Department: Computer Science
Organisation: University of Bristol
Scheme: First Grant - Revised 2009
Starts: 04 July 2016 Ends: 03 May 2018 Value (£): 98,101
EPSRC Research Topic Classifications:
Artificial Intelligence Image & Vision Computing
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
15 Mar 2016 EPSRC ICT Prioritisation Panel - Mar 2016 Announced
Summary on Grant Application Form
It is estimated that there are six million surveillance cameras in the UK, with only 17% of them publicly operated. Increasingly, people are installing CCTV cameras in their homes for security or remote monitoring of elderly, infants or pets. Despite this increase, the use of the overwhelming majority of these cameras is limited to evidence gathering or live viewing. These sensors are currently incapable of providing smart monitoring - identifying an infant in danger or a dehydrated elderly. Similarly, CCTV in public places is mostly used for evidence gathering.

Following years of research, methods capable of automatically recognising activities of interest, such as a person departing a service station without making a payment for refueling the car, or one tampering with a fuel dispenser, are now available, achieving acceptable levels of success and low false alarms. Though automatic after installation, the installation process not only requires putting the hardware in place but also involves an expert studying the footage and designing a model suitable for the monitored location. At each new location, e.g. each new service station, a new model is needed, requiring the effort and time of an expert. This is expensive, difficult to scale and at times implausible such as for home monitoring for example. This requirement to build location-specific models is currently limiting the adoption of automatic recognition of activities, despite the potential benefits.

This project, LOCATE, proposes an algorithmic solution that is capable of using a pre-built model in a different location and adapting it by simply observing the new scene for a few days. The solution is inspired by the human ability to intelligently apply previously-acquired knowledge to solve new challenges. The researchers will work with senior scientists from two leading UK video analytics industrial partners; QinetiQ and Thales. Using these partners' expertise, the project will provide practical and valuable insight that can further boost the strong UK industry of video analytics. The United Kingdom is currently a global player in the video analytics market, and the leading country in the Europe, Middle East and Africa (EMEA) region.

The method will be applicable to various domains, including for home monitoring and CCTV in public places. To evaluate the proposed approach for home monitoring, LOCATE will work alongside the EPSRC-funded project SPHERE, which aims to develop and deploy a sensor-based platform for residential healthcare in and around Bristol. The findings of LOCATE will be integrated within the SPHERE platform, towards automatic monitoring of activities of daily living in a new home, such as preparing a meal, eating or taking medication.

The targeted plug-and-play approach will enable a non-expert user to setup a camera and automatically detect whether an elderly in the home had had their meal and medication, for example. A shop owner can similarly detect pickpocketing attempts in their store. The community can thus make better use of the already in place network of visual sensors.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Project URL:  
Further Information:  
Organisation Website: http://www.bris.ac.uk