In this paper, we present joint research activities in computer vision and sensor networks for a distributedsurveillance of urban parks. Distributed visual surveillance of urban environments is one of the most interesting scenarios in Ambient Intelligence; in addition, the automated monitoring of public parks, often crowded by children and aduits, is still a very difficult task due to the number of objects of interests. In this context, integrating the power of low cost sensors with the information provided by cameras can lead to a more reliable solution to people tracking in wide areas. Specifically, the deficiencies of one approach can be (at least partially) covered by the advantages of the other. The goal is to perform people tracking in parks (toachieve trackable parks - T-Parks), both in zones covered by overlapped cameras and afso, thanks to sensors, in areas not covered by any camera. In this paper, we propose a new technique for multi-camera people tracking based on a learning phase to automatically calibrate pairs of cameras and to build Areas of Field Views (AoFoVs) in order to establish consistent labelling of people. In addition, sensornetworks distributed at the borders of the AoFoV give an estimation of the probability of people overlapping, triggering specific algorithms of face detection or headcounting to identify the single person. The research ofT-Parks is part of a two-year Italian project called LAICA, intended to provide advanced services for citizens and public officers based on ambient intelligence technologies.

T_PARK: Ambient Intelligence for Security in Public Parks / Cucchiara, Rita; Prati, Andrea; L., Benini; E., Farella. - STAMPA. - (2005), pp. 243-251. ((Intervento presentato al convegno IEE International Workshop on Intelligent Environments, Special session on "Ambient Intelligence" tenutosi a Colchester, UK nel 28-29 June 2005.

T_PARK: Ambient Intelligence for Security in Public Parks

CUCCHIARA, Rita;PRATI, Andrea;
2005

Abstract

In this paper, we present joint research activities in computer vision and sensor networks for a distributedsurveillance of urban parks. Distributed visual surveillance of urban environments is one of the most interesting scenarios in Ambient Intelligence; in addition, the automated monitoring of public parks, often crowded by children and aduits, is still a very difficult task due to the number of objects of interests. In this context, integrating the power of low cost sensors with the information provided by cameras can lead to a more reliable solution to people tracking in wide areas. Specifically, the deficiencies of one approach can be (at least partially) covered by the advantages of the other. The goal is to perform people tracking in parks (toachieve trackable parks - T-Parks), both in zones covered by overlapped cameras and afso, thanks to sensors, in areas not covered by any camera. In this paper, we propose a new technique for multi-camera people tracking based on a learning phase to automatically calibrate pairs of cameras and to build Areas of Field Views (AoFoVs) in order to establish consistent labelling of people. In addition, sensornetworks distributed at the borders of the AoFoV give an estimation of the probability of people overlapping, triggering specific algorithms of face detection or headcounting to identify the single person. The research ofT-Parks is part of a two-year Italian project called LAICA, intended to provide advanced services for citizens and public officers based on ambient intelligence technologies.
IEE International Workshop on Intelligent Environments, Special session on "Ambient Intelligence"
Colchester, UK
28-29 June 2005
243
251
Cucchiara, Rita; Prati, Andrea; L., Benini; E., Farella
T_PARK: Ambient Intelligence for Security in Public Parks / Cucchiara, Rita; Prati, Andrea; L., Benini; E., Farella. - STAMPA. - (2005), pp. 243-251. ((Intervento presentato al convegno IEE International Workshop on Intelligent Environments, Special session on "Ambient Intelligence" tenutosi a Colchester, UK nel 28-29 June 2005.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/308177
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