1. TOWARDS INTEROPERABILITY IN TRACKING SYSTEMS: AN ONTOLOGY-BASED APPROACH Juan Gómez Romero Miguel A. Patricio Jesús García José M. Molina Applied A.I. Research Group (GIAA) University Carlos III of Madrid

2. the problem To provide means to facilitate communication, interoperability, scalability and extensibility of multi-camera tracking systems

3. CS-MAS: multi-camera agent-based tracking system Fusion Agents Track. Agents Track. Agents CS-MAS

4. 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.

5. multi-camera tracking systems: CS-MAS Fusion Agents Track. Agents Track. Agents CS-MAS Variable Value Track_ID1 In_Frame2 From_CameraABX56 Width50 Height70 Pos_X324 ……

6. example The pizza delivery example

7. 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

8. 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.

9. 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

10. 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

11. 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

12. proposal Use of ontologies to describe the tracking information exchanged between the agents of CS-MAS  Tasks: Development of the T REND (Tracking Entities Description) ontology Use of the T REND ontology as the communication language of the agents

13. T REND ontology: basic classes

14. T REND ontology: track states

15. T REND ontology: properties representation

16. example: Contents of CS-MAS messages … (continues)

17. example: Contents of CS-MAS messages

18. 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 T REND in CS-MAS  Implementation of software tools exploiting T REND, 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 T REND, more abstract descriptive ontologies

19. end Thank you! jgomez@inf.uc3m.es