1 / 24

Objective

Ontological Representation of Context Knowledge for Visual Data Fusion Juan Gómez Romero Miguel A. Patricio Jesús García José M. Molina Applied Artificial Intelligence Research Group (GIAA) University Carlos III of Madrid. Objective.

bethan
Télécharger la présentation

Objective

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Ontological Representation of Context Knowledge for Visual Data FusionJuan Gómez RomeroMiguel A. PatricioJesús GarcíaJosé M. MolinaApplied Artificial Intelligence Research Group (GIAA)University Carlos III of Madrid

  2. Objective Semantic representationof visual information, both perceived and contextual,to facilitate fusion of hard and soft entriesin surveillance applications To formalize the heuristics Sensor-based data vs. Contextual and common-senseknowledge

  3. Outline • Context in Visual Data Fusion • Architecture & Contents of our Model • Conclusions and Future Work

  4. Definition of context • Context is “any information (either implicit or explicit) that can be used to characterize the situation of an entity” [1] • Computer Vision • Additional information about the scene entities [2] • Scene environment • Parameters of the recording • Previously computed information • User-provided information (soft entries!) [1] A. Dey, G. Abowd. “Towards a Better Understanding of Context and Context-Awareness,” CHI Workshop on the What, Who, Where, When, and How of Context-Awareness, The Hague, Netherlands, 2000. [2] F. Bremond, and M. Thonnat, “A context representation for surveillance systems,” ECCV Workshop on Conceptual Descriptions from Images, Cambridge, UK, 1996.

  5. Necessity of ContextKnowledgeforHigh-LevelInformationFusion L1 L2-L3 Tracking Interpretation Person 1 is (EnteringthroughEntry 2) and (ReflectedbyMirror 1) Track 008 pos () vel () Track 010 pos () vel() Person Entry > Entering Mirror > Reflection Column User-ProvidedContext Representation & Reasoning

  6. Proposal Use of ontologies to represent context knowledge for visual data fusion

  7. Ontologies for Context Management • Ontologies: “Formal, explicit specifications of a shared conceptualization” [3] • 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 • Inferences can be performed: consistency, subsumption, membership, etc. • Basic constructs: Concepts, Relations, Individuals, Axioms • Standard: The Web Ontology Language (OWL) [3] R. Studer, V. R. Benjamins, & D. Fensel. “Knowledge engineering: principles and methods”. In: Data Knowledge Engineering 25.1-2 (1998). Pp. 161–197.

  8. Proposal Use of ontologies to represent context knowledge for visual data fusion • Logic-based representation of fusion information • Associated reasoning procedures • Abstract description of the scenes • Better interpretability and easier interaction with users • Extensible and Reusable: • New elements can be easily added to the model • The model can be reused (particularly, by generalization & specialization) in different domains • Standard languages and tools • Less effort to deal with the models

  9. Contribution Ontology-based model to manage contextual and sensorial data in visual fusion systems

  10. Outline • Context in Visual Data Fusion • Architecture & Contents of the Model • Conclusions and Future Work

  11. JDL-basedarchitecture • Ontological Model • Descriptive Knowledge (TBox): Definition of concepts, relations, etc. • Intensive Knowledge (ABox): Instantiation for a concrete scene

  12. JDL-basedarchitecture: Inputs (I) Hard Inputs: Sensor Data 1. Tracking data obtained by a (classical) tracking algorithm 2. Identification data 3. Non visual sensor data

  13. JDL-basedarchitecture: Inputs (II) Soft Inputs: Human-generated Data 1. Contextual information 2. Context-based rules

  14. JDL-basedarchitecture: Outputs Outputs 1. Situation Assessment 2. Impact Assessment 3. Visualization of the interpreted situation

  15. JDL-basedarchitecture From Data to Information: Abductive Reasoning 1. Tracking: Moving entities 2. Correspondence: Association between possible objects and tracks 3. Recognition: Activity identification 4. Evaluation: Computation of the impact of an activity

  16. JDL-basedarchitecture: TREN ontology • L1 – Tracking Entites Ontology (Trend) • Ontological representation of low-level data from the tracking algorithm: frames, tracks and track properties • Temporal evolution of the tracks: tracks have associated track snapshots • Flexible representation of properties: qualia spaces (DOLCE ontology)

  17. JDL-basedarchitecture: SCOB ontology • L1-L ½ -- Scene Objects Description Ontology (Scob) • Objects of the scene: entry, exit, person, column, etc. • Static (contextual) and Dynamic (tracked) objects • Object properties (change in time)

  18. JDL-basedarchitecture: ACTV ontology • L2 – Activity Description Ontology (Actv) • Activities of the scene and connections with the objects involved: grouping activity + grouped objects • Activities taxonomy largely based on: C. Fernández, and J. González, “Ontology for Semantic Integration in a Cognitive Surveillance System,” 2nd Int. Conf. on Semantic and Digital Media Technologies, Genoa, Italy, 2007, pp. 260-263.

  19. JDL-basedarchitecture: IMPC ontology • L3 -- Impact Description Ontology (Impc) • Abstract description of the impact of activities • Impact concept (reification of the hasImpact relation) • Impact taxonomies or restrictions according to context could be implemented

  20. Overview of the In-Use OntologicalModel • Specific Model • Specialization of the template concepts provided in the General Knowledge Model • PETS2002 sequence • Abductive Rules • Rules with ontological terms to infer information of a higher level from information of a lower level • Example: • If the distance between two people is decreasing, then they are grouping

  21. Outline • Context in Visual Data Fusion • Architecture & Contents of the Model • Conclusions and Future Work

  22. Summary • Ontological model for representing contextual and perceived data for visual data fusion • Formal description of scenes and reasoning, from low-level to high-level (intra-level reasoning) • Logic-based mechanisms (rules) to infer high-level information from low-level data (inter-level reasoning) • Extensible to different applications (e.g. surveillance) • Temporal evolution of the scenes

  23. Future work • Full integration with tracking software • Adaptation (simplification) of representation and reasoning when response time is constrained • Incorporation of different data sources, not only visual • Test and validate results in different application areas • Development of ontologies and rule bases • Feedback to the low-level algorithms from the high-level • How tracking errors can be detected (or predicted) and solved when the situation has been identified?

  24. Thank You!Questions…

More Related