RT Journal Article
T1 Data fusion to improve trajectory tracking in a Cooperative Surveillance Multi-Agent Architecture
A1 Castanedo, Federico
A1 García, Jesús
A1 Patricio Guisado, Miguel Ángel
A1 Molina, José M.
AB In this paper we present a Cooperative Surveillance Multi-Agent System (CS-MAS) architecture extended to incorporate dynamic coalition formation. We illustrate specific coalition formation using fusion skills. In this case, the fusion process is divided into two layers: (i) a global layer in the fusion center, which initializes the coalitions and (ii) a local layer within coalitions, where a local fusion agent is dynamically instantiated. There are several types of autonomous agent: surveillance–sensor agents, a fusion center agent, a local fusion agent, interface agents, record agents, planning agents, etc. Autonomous agents differ in their ability to carry out a specific surveillance task. A surveillance–sensor agent controls and manages individual sensors (usually video cameras). It has different capabilities depending on its functional complexity and limitations related to sensor-specific aspects. In the work presented here we add a new autonomous agent, called the local fusion agent, to the CS-MAS architecture, addressing specific problems of on-line sensor alignment, registration, bias removal and data fusion. The local fusion agent is dynamically created by the fusion center agent and involves several surveillance–sensor agents working in a coalition. We show how the inclusion of this new dynamic local fusion agent guarantees that, in a video-surveillance system, objects of interest are successfully tracked across the whole area, assuring continuity and seamless transitions.
PB Elsevier
SN 1566-2535
YR 2010
FD 2010-07
LK https://hdl.handle.net/10016/9328
UL https://hdl.handle.net/10016/9328
LA eng
NO 13 pages, 12 figures.
NO This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505 /TIC/0255 and DPS2008-07029-C02-02.
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RD 2 may. 2024