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A semantic autonomous video surveillance system for dense camera networks in Smart Cities.

Calavia L, Baladrón C, Aguiar JM, Carro B, Sánchez-Esguevillas A - Sensors (Basel) (2012)

Bottom Line: This paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement.Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies.This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network.

View Article: PubMed Central - PubMed

Affiliation: Universidad de Valladolid, Dpto. TSyCeIT, ETSIT, Paseo de Belén 15, Valladolid 47011, Spain. lcaldom@ribera.tel.uva.es

ABSTRACT
This paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be deployed on the system, and therefore making it suitable for its usage as an integrated safety and security solution in Smart Cities. Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies. This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network.

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Related in: MedlinePlus

Frame processing time against number of objects and routes in the image and points-per-trajectory parameter.
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f10-sensors-12-10407: Frame processing time against number of objects and routes in the image and points-per-trajectory parameter.

Mentions: For each test, 50 frames were generated with random locations for the appropriate number of objects and routes, and then processed with the semantic engine in the same machine used for the route detector evaluation, an Intel Pentium 4 3.00 GHz with 2 Gb RAM. Figure 10 presents the average processing time for each frame.


A semantic autonomous video surveillance system for dense camera networks in Smart Cities.

Calavia L, Baladrón C, Aguiar JM, Carro B, Sánchez-Esguevillas A - Sensors (Basel) (2012)

Frame processing time against number of objects and routes in the image and points-per-trajectory parameter.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472835&req=5

f10-sensors-12-10407: Frame processing time against number of objects and routes in the image and points-per-trajectory parameter.
Mentions: For each test, 50 frames were generated with random locations for the appropriate number of objects and routes, and then processed with the semantic engine in the same machine used for the route detector evaluation, an Intel Pentium 4 3.00 GHz with 2 Gb RAM. Figure 10 presents the average processing time for each frame.

Bottom Line: This paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement.Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies.This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network.

View Article: PubMed Central - PubMed

Affiliation: Universidad de Valladolid, Dpto. TSyCeIT, ETSIT, Paseo de Belén 15, Valladolid 47011, Spain. lcaldom@ribera.tel.uva.es

ABSTRACT
This paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be deployed on the system, and therefore making it suitable for its usage as an integrated safety and security solution in Smart Cities. Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies. This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network.

Show MeSH
Related in: MedlinePlus