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Suspicious and Abnormal Behaviour Monitoring  Using a Network of Cameras & Sensors for Situation Awareness Enhancement

Suspicious and Abnormal Behaviour Monitoring Using a Network of Cameras & Sensors for Situation Awareness Enhancement

Objective

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 site. 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 unacceptable 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 alarm. *In addition to fix-positioned CCTV cameras, the SAMURAI system will also take command input from control room 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 system that relies purely on information extracted from the video data, often too ambiguous to be effective. SAMURAI has the following scientific objectives: 1. Develop innovative tools and systems for people, vehicle and luggage detection, tracking, type categorisation across a network of cameras under real world conditions. 2. Develop an abnormal behaviour 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 awarenes. 3. Develop innovative tools using multi-modal data fusion and visualisation of heterogeneous sensor input to enable more effective control room operator queries.
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Coordinator

QUEEN MARY UNIVERSITY OF LONDON

Address

327 Mile End Road
E1 4ns London

United Kingdom

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 762 237

Administrative Contact

Shaogang Gong (Prof.)

Participants (7)

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UNIVERSITA DEGLI STUDI DI VERONA

Italy

EU Contribution

€ 333 000

SELEX ELSAG SPA

Italy

EU Contribution

€ 426 095

Waterfall Solutions Ltd

United Kingdom

EU Contribution

€ 225 391

Borthwick-Pignon OÜ

Estonia

EU Contribution

€ 357 557

ESAPROJEKT SP Z OO

Poland

EU Contribution

€ 158 937,50

SYNDICAT MIXTE DES TRANSPORTS POURLE RHONE ET L AGGLOMERATION LYONNAISE

France

EU Contribution

€ 60 804

LHR AIRPORTS LIMITED

United Kingdom

EU Contribution

€ 154 030

Project information

Grant agreement ID: 217899

Status

Closed project

  • Start date

    1 June 2008

  • End date

    30 November 2011

Funded under:

FP7-SECURITY

  • Overall budget:

    € 3 723 071,40

  • EU contribution

    € 2 478 052

Coordinated by:

QUEEN MARY UNIVERSITY OF LONDON

United Kingdom

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