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Description Logic for Vision-Based Intersection Interpretation

Description Logic for Vision-Based Intersection Interpretation. Britta Hummel. Motivation. Road Recognition: The „Model-based“ Approach. 1. Project. Low-dim. geometry model (clothoid, …). 2. Compare. 3. Update Parameters. Solved for highly constrained domains (highways). Motivation.

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Description Logic for Vision-Based Intersection Interpretation

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  1. Description Logic for Vision-Based Intersection Interpretation Britta Hummel

  2. Motivation Road Recognition: The „Model-based“ Approach 1. Project Low-dim. geometry model (clothoid, …) 2. Compare 3. Update Parameters • Solved for highly constrained domains (highways)

  3. Motivation Intersection Recognition: [Heimes&Nagel02] 1. Project 2. Compare 3. Update Parameters • How can we generalize to arbitrary intersections?

  4. High-dimensional hypothesis space 2. Few features - Narrow field of view - Massive occlusions - Omitted features Presence of noise - Unmodelled objects - Bad feature quality Motivation Challenges • Model-based approach becomes ill-posed!

  5. Motivation So …what now? • More top-down information flow  start higher up: use conceptual knowledge!  move further down: parameterize feature detectors! • Collective classification  simultaneously reconstruct geometry and semantics! • Narrow down hypothesis space! • FOL Representation and FOL Reasoning!

  6. This Talk • Motivation • Architecture • DL Road Network KB • DL Inference for Scene Interpretation • Application • Evaluation

  7. Generate Constrain Logical „Configuration“ Model Generic Geometry Model DL Road Network KB Verify/Falsify Learn Architecture Enhance Model-Based Vision by Logic Feature detectors, other KB‘s, … Project Update Pars

  8. This Talk • Motivation • Architecture • DL Road Network KB • DL Inference for Scene Interpretation • Application • Evaluation

  9. Model of Geometry Geometric Primitives GP1 GP3 GP2 Spatial Relations

  10. Symbol Grounding Geometric Primitives Spatial Relations

  11. TBox Geometric Constraints

  12. TBox Constraints wrt Road Building Regulations

  13. ABox Sensor Data Integration • Partial observability  OWA • Structurally differing sensor data (e.g. from map, video) • Distributed sensor data • Non-UNA + identification reasoning • Open/Closed Domain Data • (Nominals) / Closed domain assumption: • Conflicting/Uncertain Data  BLPs/MLNs/…

  14. This Talk • Motivation • Architecture • DL Road Network KB • DL Inference for Scene Interpretation • Application • Evaluation

  15. Inference: Example I (Collective) Classification is Abox realization l21 l22 l13 l12 l11

  16. Inference: Example I (Collective) Classification is Abox realization l21 l22 l13 l12 l11 16

  17. Inference: Example I (Collective) Classification is Abox realization l21 l22 tr-l11-l21 l13 l12 l11 17

  18. Inference: Example I (Collective) Classification is Abox realization l22 l21 tr-l11-l21 l13 l12 l11 …

  19. Inference: Example I Link Prediction is Instance Checking 19

  20. Inference: Example II Link Prediction is Instance Checking l22 l21 tr-l11-l21 l13 l12 l11

  21. Inference: Example III Data Association is Identification Reasoning Positioning Device & Map Matching: Video: Digital Map: 

  22. Inference: Example IV Hypothesis Generation is …? • Classical logical inference is deductive • Bio./Mach. Vision is not deductive: lots of hypothetical reasoning, jumping to conclusions, backtracking if wrong  Non-deductive / non-monotonic reasoning needed! …Abduction Poole, Shanahan, Möller …Introducing procedurality [Neumann&Möller06] …Model construction by transformation into Constraint Satisfaction Pr. [Reiter&Mackworth87] …Model construction under Answer Set Semantics We have started…

  23. Beautiful Analogies…

  24. This Talk • Motivation • Architecture • DL Road Network KB • DL Inference for Scene Interpretation • Application • Evaluation

  25. Application Geometry model generated from DL ground truth ABox

  26. Application

  27. This Talk • Motivation • Architecture • DL Road Network KB • DL Inference for Scene Interpretation • Application • Evaluation

  28. Road Recognition  Intersection Interpretation escape from toy world  narrow down hypothesis space: not only bottom-up but also top-down reasoning collective classification Enhance model-based vision by logical reasoning Expressive geometry model Generate generic geometric model out of logical „configuration“ model Generate and constrain logical model through logical reasoning Summary

  29. Vision: Sets of knowledge engineers coding&maintaining large, distributed, modular, semantically unambiguous KB‘s for SI DL: Wish List  : Foundational ontologies / „best practices“ for KB design for SI Faster Abox reasoning (>10 individuals prohibitively slow on our KB) Language expressiveness: Spatial Relations: JEPD condition Feature chains Nominals Nonmonotonic reasoning Evaluation

  30. Nonmonotonic reasoning with ASP Incremental hypothesize & test Integration with Irina Lulcheva‘s MLN-based traffic participant classificator Rule Learning from Training Data Outlook

  31. Thanks  Thanks…

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