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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

Two example applications (one over a synthetic movie, one over a real movie) of Route Detection. Green and blue lines represent the center line of routes, with yellow and red lines representing the envelopes of the routes. Cyan and magenta “x” points are entry and exit points respectively (a point where an object has appeared or disappeared). White squares are sources (clusters of entry points).
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f2-sensors-12-10407: Two example applications (one over a synthetic movie, one over a real movie) of Route Detection. Green and blue lines represent the center line of routes, with yellow and red lines representing the envelopes of the routes. Cyan and magenta “x” points are entry and exit points respectively (a point where an object has appeared or disappeared). White squares are sources (clusters of entry points).

Mentions: Each route is characterized as a strip: a sequence of center points and two envelopes, as shown in the Figure 2 (the sequence of center points represented by green lines, and the envelopes in yellow lines), marked with a direction (in the figure, the direction of the route is marked with a sharper arrow end). This means that a reversible lane will be represented by two routes, one for each direction. The envelopes are obtained using the width and height values given by the camera.


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)

Two example applications (one over a synthetic movie, one over a real movie) of Route Detection. Green and blue lines represent the center line of routes, with yellow and red lines representing the envelopes of the routes. Cyan and magenta “x” points are entry and exit points respectively (a point where an object has appeared or disappeared). White squares are sources (clusters of entry points).
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-12-10407: Two example applications (one over a synthetic movie, one over a real movie) of Route Detection. Green and blue lines represent the center line of routes, with yellow and red lines representing the envelopes of the routes. Cyan and magenta “x” points are entry and exit points respectively (a point where an object has appeared or disappeared). White squares are sources (clusters of entry points).
Mentions: Each route is characterized as a strip: a sequence of center points and two envelopes, as shown in the Figure 2 (the sequence of center points represented by green lines, and the envelopes in yellow lines), marked with a direction (in the figure, the direction of the route is marked with a sharper arrow end). This means that a reversible lane will be represented by two routes, one for each direction. The envelopes are obtained using the width and height values given by the camera.

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