SAMURAI – Suspicious and Abnormal behaviour Monitoring Using a netwoRk of cAmeras for sItuation awareness enhancement
SAMURAI – Suspicious and Abnormal behaviour Monitoring Using a netwoRk of cAmeras for sItuation awareness enhancement
SAMURAI is a collaborative project funded under the European Commission Seventh Framework Programme Theme 10 (Security). The aim of SAMURAI is to develop and integrate an innovative intelligent surveillance system for monitoring people and vehicle activities at both inside and surrounding areas of a critical public infrastructure.
The project consortium consists of 8 partners across the EU, led by Queen Mary, University of London with Professor Shaogang Gong as the project co-ordinator.
|
Beneficiary Name |
Acroynm |
Country |
Co-Ordinator |
|
Queen Mary University of London: |
QMUL |
UK (United Kingdom) |
Prof Shaogang Gong |
|
Università di Verona: |
UNIVR |
IT (Italy) |
Prof Vittorio Murino |
|
ED |
IT (Italy) |
Giovanni Garibotto |
|
|
WS |
UK (United Kingdom) |
Dr Duncan Hickman |
|
|
BPS |
EE (Estonia) |
Leon Borthwick |
|
|
ESA |
PL (Poland) |
Katarzyna Ptasznik |
|
|
Syndicat Mixte des Transports pour le Rhône et l’Agglomération Lyonnaise |
SYTRAL |
FR (France) |
Michel Rodet |
|
BAA |
UK (United Kingdom) |
Huw Farmer |
Project Aims
The aim of SAMURAI is to develop and integrate an innovative intelligent surveillance system for robust monitoring of both inside and surrounding areas of a critical public infrastructure. SAMURAI has three significant novelties that make it distinctive from other recent and ongoing relevant activities both in the EU and elsewhere: *SAMURAI is to employ networked heterogeneous sensors rather than CCTV cameras alone so that multiple complementary sources of information can be fused to create a visualisation of a more complete ‘big picture’ of a crowded public space. * Existing systems focus on analysing recorded video using pre-defined hard rules, suffering from unaccepted false alarms. SAMURAI is to develop a real-time adaptive behaviour profiling and abnormality detection system for alarm event alert and prediction with much reduced false operators and mobile sensory input for patrolling security staff for a hybrid context-aware based abnormal behaviour recognition. This is in contrary to current video behaviour recognition systems that rely purely on information extracted from the video data, often too ambiguous to be effective.
SAMURAI has the following scientific objectives :
-
Develop innovative tools and systems for people, vehicle and luggage detection, tracking, type categorisation across a network of cameras under real world conditions.
-
Develop an abnormal detection system based on a heterogeneous sensor network consisting of both fix-positioned CCTV cameras and mobile wearable cameras with audio and positioning sensors. These networked heterogeneous sensors will function cooperatively to provide enhanced situation awareness.
-
Develop innovative tools using multi-modal data fusion and visualisation of heterogeneous sensor input to enable more effective control room operator queries.
The Team
Prof Sean Gong ………… Project Co-Ordinator …………
Dr Tony Xiang………… Assistant Project Co-Ordinator …………
Catherine Edlin………… Project Administrator …………
Adam Smith
BAA Limited
Alan Gunn
BAA Limited
Alberto Bianchi
Elsag Datamat
Andrea Fusiello
University of Verona
Ben James
Waterfall Solutions
Bogusław Legierski
Esaprojekt
Bob Koger
BPS
Catherine Edlin
Queen Mary University of London
Caroline Wardle
Queen Mary University of London
Dean Pignon
BPS
Duncan Hickman
Waterfall Solutions
Franco Selvaggi
Elsag Datamat
Giovanni Garibotto
Elsag Datamat
Gisella Corbellini
Elsag Datamat
Huw Farmer
BAA Limited
Weishi Zheng
Queen Mary University of London
Leon Borthwick
BPS
Leonardo Roncarolo
Elsag Datamat
Marco Cristani
University of Verona
Michela Farenzena
University of Verona
Maciej Kotok
Esaprojekt
Moira Smith
Waterfall Solutions
Eve-Marie Moos
SYTRAL
Paul Pignon
BPS
Piotr Góral
Esaprojekt
Rafal Dunal
Esaprojekt
Michel Rodet
SYTRAL
Scott Page
Waterfall Solutions
Sean Gong
Queen Mary University of London
Stephen Challis
BAA Limited
Timothy Hospedales
Queen Mary University of London
Tony Xiang