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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

Unclassified individual.
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f5-sensors-12-10407: Unclassified individual.

Mentions: Figure 4 shows the hierarchical class structure of an example traffic domain ontology designed to test the system. Figure 5 shows the description of an individual with its own characteristics prior to its classification by Pellet. Figure 6 shows how this object has been classified according to the semantic rules specified, with two new classes to which the object belongs to appearing in its description. Firstly, it is classified as “bus” within the “vehicle” category and, in addition, it is classified as “exceededSpeed”, within the “Alarm” class, as the appropriate conditions are met.


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)

Unclassified individual.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-12-10407: Unclassified individual.
Mentions: Figure 4 shows the hierarchical class structure of an example traffic domain ontology designed to test the system. Figure 5 shows the description of an individual with its own characteristics prior to its classification by Pellet. Figure 6 shows how this object has been classified according to the semantic rules specified, with two new classes to which the object belongs to appearing in its description. Firstly, it is classified as “bus” within the “vehicle” category and, in addition, it is classified as “exceededSpeed”, within the “Alarm” class, as the appropriate conditions are met.

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