towards interoperability in tracking systems: an ontology-based approach juan gómez romero miguel...
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TOWARDS INTEROPERABILITY IN TRACKING SYSTEMS:AN ONTOLOGY-BASED APPROACH
Juan Gómez RomeroMiguel A. PatricioJesús GarcíaJosé M. Molina
Applied A.I. Research Group (GIAA)University Carlos III of Madrid
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the problem
To provide means to facilitate communication, interoperability,
scalability and extensibility of multi-camera tracking systems
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CS-MAS: multi-camera agent-based tracking system
FusionAgents
Track.Agents
Track.Agents
CS-MAS
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data communication
Tracking data: Track identification Physical properties (2D or 3D-space):
Size, Position, Color, Velocity, etc.
Estimated properties (Kalman, etc.) Size, Position, Velocity, etc.
State Active, Occluded, Grouped, etc.
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multi-camera tracking systems: CS-MAS
FusionAgents
Track.Agents
Track.Agents
CS-MAS
Variable Value
Track_ID 1
In_Frame 2
From_Camera ABX56
Width 50
Height 70
Pos_X 324
… …
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example
The pizza delivery example
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communication problems
Problem: Misunderstandings!
Different individuals involved: Different vocabulary Different assumptions Different background knowledge
Solution: Use a formal language to describe pizzas
Knowledge representation: Ontologies
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ontologies
“Formal, explicit specifications of a shared conceptualization” [1] An ontology is a knowledge model which describes from a common
perspective the objects in a common domain using a language that can be processed automatically
Based on Description Logics (DLs) DLs are a family of logics to represent structured knowledge
Basic constructs: Concepts, Relations, Individuals, Axioms
Standard The Web Ontology Language (OWL)
[1] R. Studer, V. R. Benjamins, & D. Fensel. “Knowledge engineering: principles and methods”. In: Data Knowledge Engineering 25.1-2 (1998). Pp. 161–197.
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example: the pizza ontology
Manchester Pizza Ontology: http://www.co-ode.org/ontologies/pizza/pizza.owl
American Pizza Class: http://www.co-ode.org/ontologies/pizza/pizza.owl#American Is a:
NamedPizza hasCountryOfOrigin value America hasTopping only
(MozzarellaTopping or PeperoniSausageTopping or TomatoTopping)
hasTopping some MozzarellaTopping hasTopping some PeperoniSausageTopping hasTopping some TomatoTopping
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advantages of the use of ontologies
Understanding among agents: Different interpretations are not possible
Decoupling of internal and external representations A pizza image can have associated a formal description
Extensibility of the architecture Different pizza companies can communicate; delivery
could be extended between districts
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advantages of the use of ontologies
Obtaining implicit knowledge by reasoning Pepper is a Spicy ingredient; pizzas with pepper are Spicy
pizzas
Support for high-level information interpretation It can be deduced, using DL inference, that a client likes spicy
pizzas and special offers can be sent
Improved data manipulation and querying Ontologies have associated query languages (e.g. SPARQL)
Implementation of mash-up applications A web page with suggestions to the clients based on their
preferences
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proposal
Use of ontologies to describe the tracking information exchanged
between the agents of CS-MAS
Tasks: Development of the TREND (Tracking Entities
Description) ontology Use of the TREND ontology as the communication
language of the agents
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TREND ontology: basic classes
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TREND ontology: track states
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TREND ontology: properties representation
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example: Contents of CS-MAS messages
<Track rdf:about="#track12"> <hasSnapshot rdf:resource="#snapshot_A"/></Track>
<TrackSnapshot rdf:about="#snapshot_12_A "> <rdf:type rdf:resource="#ActiveTrackSnapshot"/> <isValidIn rdf:resource="#frame1"/> <isValidIn rdf:resource="#frame2"/> <hasActualProperties rdf:resource="#actual_properties_12_A"/> <hasPredictedProperties rdf:resource="#predicted_properties_12_A "/></TrackSnapshot>
… (continues)
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example: Contents of CS-MAS messages
<Track rdf:about="#track12"> <hasSnapshot rdf:resource="#snapshot_B"/></Track>
<TrackSnapshot rdf:about="#snapshot_12_B "> <rdf:type rdf:resource="#ActiveTrackSnapshot"/> <isValidIn rdf:resource="#frame3"/> <hasActualProperties rdf:resource="#actual_properties_12_B"/> <hasPredictedProperties rdf:resource="#predicted_properties_12_B "/></TrackSnapshot>
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summary & future work
Ontology for describing the tracking data interchanged by the agents of CS-MAS (a multi-camera tracking system)
Common vocabulary advantages: understandability, extensibility, interoperability
Research directions: Fully integration of TREND in CS-MAS Implementation of software tools exploiting TREND, e.g. a visualization
tool to present the temporal evolution of tracks of the image High-level interpretation of data
Interpretation of the scene in terms of objects, events, etc.
Define, based on TREND, more abstract descriptive ontologies