FP7 Dynamic Database
|Name||Suspicious and abnormal behaviour monitoring using a network of cameras & sensors for situation awareness enhancement|
|Area||SEC-2007-2.3-04 Small area 24 hours surveillance,SEC-2007-2.3-03 Detection of abnormal behaviour|
|Description||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:
- 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 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.
- Develop innovative tools using multi-modal data fusion and visualisation of heterogeneous sensor input to enable more effective control room operator queries.
|Coordinator||QUEEN MARY AND WESTFIELD COLLEGE, UNIVERSITY OF LONDON,Mile End Road,UNITED KINGDOM|
|Contact||GONG, Shaogang (Professor) ( Contact )|
|Tel / Fax||+44-2078825249 / +44-2089806533|
|partners||BORTHWICK-PIGNON O (ESTONIA)|
ELSAG DATAMAT S.P.A. (ITALY)
BAA LIMITED (UNITED KINGDOM)
SYNDICAT MIXTE DES TRANSPORTS POURLE RHONE ET L AGGLOMERATION LYONNAISE (FRANCE)
WATERFALL SOLUTIONS LTD (UNITED KINGDOM)
UNIVERSITA DEGLI STUDI DI VERONA (ITALY)
ESAPROJEKT SP Z OO (POLAND)
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