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ORION Project-team

ORION Project-team. Monique THONNAT INRIA Sophia Antipolis. Creation: July 1995 Multidisciplinary team : artificial intelligence, software engineering, computer vision. Contents. Team Presentation Research Directions Cognitive Vision 2002-2006 Reusable Systems 2002-2006

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ORION Project-team

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  1. ORION Project-team Monique THONNAT INRIA Sophia Antipolis Creation: July 1995 Multidisciplinary team: artificial intelligence, software engineering, computer vision

  2. Contents • Team Presentation • Research Directions • Cognitive Vision 2002-2006 • Reusable Systems 2002-2006 • Objectives for the next Period Orion

  3. Team presentation (May 2006) 4 Research Scientists: François Bremond (CR1 Inria) Sabine Moisan (CR1 Inria, HDR) Annie Ressouche (CR1 Inria) (team leader) Monique Thonnat (DR1 Inria) 1 External Collaborator: Jean-Paul Rigault (Prof. UNSA Inria secondment) 4 Temporary Engineers: Etienne Corvee, Ruihua Ma, Valery Valentin, Thinh Van Vu 7 PhD Students: Bui Binh, Bernard Boulay, Naoufel Kayati, Le Thi Lan, Mohamed Becha Kaaniche, Vincent Martin, Marcos Zuniga Orion

  4. Research directions Objective: Intelligent Reusable Systems for Cognitive Vision Cognitive Vision: • Interpretation of static images • Video understanding Reusable Systems: • Program Supervision • LAMA Software platform Orion

  5. Orion team positioning Cognitive Vision: • Image interpretation (ECVision European network on cognitive vision, EUCognition) vs. computer vision (INRIA CogB) • Video understanding (USC Los Angeles, Georgia Tech. Atlanta, Univ. Central Florida, NUCK Taiwan, Univ. Kingston UK, INRIA Prima) Reusable Systems: • Program supervision: e.g., scheduling (ASPEN and CASPER at JPL), image processing (Hermès at Univ. Caen, ExTI at IRIT)… • Platform approach: e.g., ontology management (Protegé at Stanford), frameworks for multi agents (Aglets, Jade, Oasis at LIP6), distributed object community (Oasis at INRIA Sophia)… Orion

  6. Cognitive Vision : Image Interpretation 2002-2006 Objective: semantic interpretation of static 2D images • Recognition of object categories (versus individuals) • Recognition of scenes involving several objects with spatial reasoning • Intelligent management of image processing programs Towards a cognitive vision platform Orion

  7. Cognitive Vision : Image Interpretation 2002-2006 Scientific achievements: • Knowledge acquisition: • A visual concept ontology with 144 spatial, color and texture concepts [MVA04] • Learning: • Visual concept detectors [IVC06] • Image segmentation parameters [ICVSa06] • Cognitive vision platform • Architecture [ICVS03] • Object class recognition algorithm [CIVR05] Orion

  8. Cognitive Vision: Image Interpretation 2002-2006 Self Assessment: • Strong points: • Visual concept ontology as user-friendly intermediate layer between image processing and application domain • Automatic building of the visual concept detectors • Still open issues: • Learning for image segmentation • Temporal visual concept ontology Orion

  9. Cognitive Vision: Video Understanding 2002-2006 Objective: • Real time recognition of interesting behaviors How? • Data captured by video surveillance cameras • Original video understanding approach mixing: • computer vision:4D analysis (3D + temporal analysis) • artificial intelligence:a priori knowledge (scenario, environment) • software engineering: reusable VSIP platform Orion

  10. Cognitive Vision: Video Understanding 2002-2006 Objective: Interpretation of videos from pixels to alarms Segmentation Classification Scenario Recognition Tracking Alarms access to forbidden area 3D scene model Scenario models A priori Knowledge Orion

  11. Cognitive Vision: Video Understanding 2002-2006 Scientific achievements: • Multi-sensor video understanding: • 2 to 4 video cameras overlapping or not [IDSS03,JASP05] • Video cameras + optical cells + contact sensors [AVSS05]… • Learning: • parameter tuning[MVAa06] • frequent temporal scenarios models [ICVSb06] • Temporal scenario: • a new real time recognition algorithm [IJCAI03,ICVS03] • a new representation language [MVAb06,ECAI02,KES02] Orion

  12. Cognitive Vision: Video Understanding 2002-2006 Industrial impact: • Strong impact in visual surveillance (metro station, bank agency, building access control, onboard train, airport) • 4 European projects (ADVISOR, AVITRACK, SERKET, CARETAKER) • 5 industrial contracts with RATP, ALSTOM, SNCF, Credit Agricole, STMicroelectronics • 2 transfer activities with BULL (Paris), VIGITEC (Brussels) • Creation of a start-up Keeneo July 2005 (8 persons) for industrialization and exploitation of VSIP library. Orion

  13. Cognitive Vision: Video Understanding 2002-2006 Intelligent video surveillance of Bank agencies Orion

  14. Cognitive Vision: Video Understanding 2002-2006 • “Unloading Global Operation” Toulouse - 3rd June 2004 Orion

  15. Cognitive Vision: Video Understanding 2002-2006 Airport Apron Monitoring “Unloading Operation” European AVITRACK project Toulouse - 3rd June 2004 Orion

  16. Cognitive Vision:Video Understanding 2002-2006 Self Assessment: • Strong points: • Video understanding approach: real time, effective techniques used by external academic and industrial teams • Launch of an evaluation competition for video surveillance algorithms (ETISEO) with currently 25 international teams • Still open issues: • Learning • Multi sensor Orion

  17. Reusable Systems: Program Supervision Reusable Systems: original approach for the reuse of programs with program supervision techniques Program supervision: Automate the (re)configuration and execution of programs • selection, scheduling, execution, and control of results Knowledge-based approach: knowledge modeling, planning techniques, ….. Orion

  18. Reusable Systems: LAMA Platform Reusable Systems: Reuse of tools to design knowledge-based systems (KBS) LAMA Software Platform: Set of toolkits to facilitate design and evolution of KBS elements: • engines, GUI, knowledge languages, learning and verification facilities… Software Engineering approach:genericity, frameworks, objects and components LAMA raise new issues, to be abstracted into new components provide generic components and tools Problem Solving KBS Virtuous Circle Orion

  19. LAMA Designer Expert Java graphic library for GUIs Task dedicated GUI Task dedicated Language with compiler & KB verification Compilers/verifiers generators for knowledge description languages Verification library for knowledge bases Program Supervision Framework for engine design & knowledge representation support and task specific layers Knowledge Base Task dedicated Engine Object Recognition Blocks KBS Model Calibration User Reusable Systems: LAMA Platform Orion

  20. Reusable Systems: Program Supervision 2002-2006 Scientific achievements: • Improvement of the Pegase engine (Pegase+) • Multithreading, extensions to the YAKL language [ECAI02] • Distributed program supervision • Supervision Web server, multi-agent techniques, interoperability Pegase/Java/agents [TC06] • Cooperation with image and video understanding • Object recognition task using program supervision [ICTAI03] • Interoperability with VSIP: program supervision for video understanding [ICVSc06] Orion

  21. Reusable Systems: LAMA Platform 2002-2006 Scientific achievements: • Enforcing LAMA safe usage • Verification of LAMA component extensions relying on Model Checking approach [Informatica01, SEFM04] • Encompassing new tasks • Classification and object recognition in images: new engine and new knowledge representation language [ICTAI03] • Model calibration in hydraulics: new engine/language (PhD co-directed with INPT and CEMAGREF) [KES03, JH05] Orion

  22. Reusable Systems: Self Assessment Strong points: • Real time performance (Pegase+ and video) • Using program supervision costs less than 5% of overall processing time • LAMA genericity at work • Different tasks (supervision, classification, calibration) in various application domains (hydraulics, biology, astronomy, video surveillance…) • Shorter development time and safer code • Reuse of concepts as well as code • Several variants of a task sharing common concepts • Extensibility and commitment to Standards Orion

  23. Objectives for the next period 1/5 Creation of a new INRIA project-teamPULSAR Perception Understandingand Learning Systems for Activity Recognition Theme: CogC Multimedia data: interpretation and man-machine interaction Multidisciplinary team: artificial intelligence, software engineering, computer vision Objective: • Research on Cognitive Systems for Activity Recognition • Focus on spatiotemporal activities of physical objects • From sensor output to high level interpretation Orion

  24. Objectives for the next period 2/5 PULSAR Scientific objectives: Two research axes: • Scene Understanding for Activity Recognition • Generic Components for Activity Recognition PULSAR Applications: • Safety/security (e.g. intelligent surveillance) • Healthcare (e.g. assistance to the elderly) Orion

  25. Objectives for the next period 3/5 PULSAR: Scene Understanding for Activity Recognition • Perception: multi-sensors, finer descriptors • Understanding: uncertainty, 4D coherency, ontology for AR • Learning: parameter setting, event detector, activity models, program supervision KB (risky objective) Orion

  26. Objectives for the next period 4/5 PULSAR Generic Components for Activity Recognition From LAMA Platform to AR platform: • Model extensions: • modeling time and scenarios • handling uncertainty • User-friendliness and safeness of use: • theory and tools for component frameworks • scalability of verification methods • Architecture improvement: • parallelization, distribution, concurrence • real time response • domain specific software and graphical interface plugging Orion

  27. Objectives for the next period 5/5 Short term objectives: Scene Understanding for Activity Recognition • Perception: gesture analysis • Understanding: • ontology-based activity recognition • uncertainty management • Learning: primitive event detectors learning Generic Components for Activity Recognition • Model of time and scenarios • Internal concurrency and distributed architecture Orion

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