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Putting Objects in Perspective

Putting Objects in Perspective. Derek Hoiem Alexei A. Efros Martial Hebert Carnegie Mellon University Robotics Institute. Understanding an Image. Today: Local and Independent. What the Detector Sees. Local Object Detection. True Detection. False Detections. Missed. Missed.

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Putting Objects in Perspective

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  1. Putting Objects in Perspective Derek Hoiem Alexei A. Efros Martial Hebert Carnegie Mellon University Robotics Institute

  2. Understanding an Image

  3. Today: Local and Independent

  4. What the Detector Sees

  5. Local Object Detection True Detection False Detections Missed Missed True Detections Local Detector: [Dalal-Triggs 2005]

  6. Work in Context • Image understanding in the 70’s Guzman (SEE) 1968 Hansen & Riseman (VISIONS) 1978 Barrow & Tenenbaum 1978 Yakimovsky & Feldman 1973 Brooks (ACRONYM) 1979 Marr 1982 Ohta & Kanade 1973 • Recent work in 2D context He, Zemel, Cerreira-Perpiñán 2004 Carbonetto, Freitas, Banard 2004 Winn & Shotton 2006 Kumar & Hebert 2005 Torralba, Murphy, Freeman 2004 Fink & Perona 2003

  7. Real Relationships are 3D Close Not Close

  8. Recent Work in 3D [Han & Zu 2003] [Oliva & Torralba 2001] [Torralba, Murphy & Freeman 2003] [Han & Zu 2005]

  9. Objects and Scenes • Biederman’s Relations among Objects in a Well-Formed Scene (1981): Hock, Romanski, Galie, & Williams 1978 • Support • Size • Position • Interposition • Likelihood of Appearance

  10. Contribution of this Paper • Biederman’s Relations among Objects in a Well-Formed Scene (1981): Hock, Romanski, Galie, & Williams 1978 • Support • Size • Position • Interposition • Likelihood of Appearance

  11. Object Support

  12. Object Surface? Support? Surface Estimation Image Support Vertical Sky V-Center V-Right V-Porous V-Solid V-Left [Hoiem, Efros, Hebert ICCV 2005] Software available online

  13. Object Size in the Image Image World

  14. Object Size ↔ Camera Viewpoint Input Image Loose Viewpoint Prior

  15. Object Size ↔ Camera Viewpoint Input Image Loose Viewpoint Prior

  16. Object Size ↔ Camera Viewpoint Viewpoint Object Position/Sizes

  17. Object Size ↔ Camera Viewpoint Viewpoint Object Position/Sizes

  18. Object Size ↔ Camera Viewpoint Viewpoint Object Position/Sizes

  19. Object Size ↔ Camera Viewpoint Viewpoint Object Position/Sizes

  20. P(surfaces) P(viewpoint) P(object | surfaces) P(object | viewpoint) What does surface and viewpoint say about objects? Image P(object)

  21. What does surface and viewpoint say about objects? Image P(surfaces) P(viewpoint) P(object | surfaces, viewpoint) P(object)

  22. Scene Parts Are All Interconnected Objects 3D Surfaces Camera Viewpoint

  23. Input to Our Algorithm Object Detection Surface Estimates Viewpoint Prior Local Car Detector Local Ped Detector Local Detector: [Dalal-Triggs 2005] Surfaces: [Hoiem-Efros-Hebert 2005]

  24. Scene Parts Are All Interconnected Objects 3D Surfaces Viewpoint

  25. Our Approximate Model Objects 3D Surfaces Viewpoint

  26. Inference over Tree Easy with BP Viewpoint θ Local Object Evidence Local Object Evidence Objects ... o1 on Local Surface Evidence Local Surface Evidence Local Surfaces … s1 sn

  27. Viewpoint estimation Viewpoint Prior Viewpoint Final Likelihood Likelihood Horizon Height Horizon Height

  28. Object detection Car: TP / FP Ped: TP / FP Initial (Local) Final (Global) Car Detection 4 TP / 1 FP 4 TP / 2 FP Ped Detection 4 TP / 0 FP 3 TP / 2 FP Local Detector: [Dalal-Triggs 2005]

  29. Experiments on LabelMe Dataset • Testing with LabelMe dataset: 422 images • 923 Cars at least 14 pixels tall • 720 Peds at least 36 pixels tall

  30. Each piece of evidence improves performance Pedestrian Detection Car Detection Local Detector from [Murphy-Torralba-Freeman 2003]

  31. Can be used with any detector that outputs confidences Car Detection Pedestrian Detection Local Detector: [Dalal-Triggs 2005] (SVM-based)

  32. Accurate Horizon Estimation [Dalal- Triggs 2005] [Murphy-Torralba-Freeman 2003] Horizon Prior Median Error: 8.5% 4.5% 3.0% 90% Bound:

  33. Qualitative Results Car: TP / FP Ped: TP / FP Initial: 2 TP / 3 FP Final: 7 TP / 4 FP Local Detector from [Murphy-Torralba-Freeman 2003]

  34. Qualitative Results Car: TP / FP Ped: TP / FP Initial: 1 TP / 14 FP Final: 3 TP / 5 FP Local Detector from [Murphy-Torralba-Freeman 2003]

  35. Qualitative Results Car: TP / FP Ped: TP / FP Initial: 1 TP / 23 FP Final: 0 TP / 10 FP Local Detector from [Murphy-Torralba-Freeman 2003]

  36. Qualitative Results Car: TP / FP Ped: TP / FP Initial: 0 TP / 6 FP Final: 4 TP / 3 FP Local Detector from [Murphy-Torralba-Freeman 2003]

  37. meters meters Summary & Future Work Ped Ped Car • Reasoning in 3D: • Object to object • Scene label • Object segmentation

  38. Conclusion • Image understanding is a 3D problem • Must be solved jointly • This paper is a small step • Much remains to be done

  39. Thank you

  40. Guzman (SEE), 1968 Hansen & Riseman (VISIONS), 1978 Barrow & Tenenbaum 1978 Brooks (ACRONYM), 1979 Marr, 1982 Ohta & Kanade, 1978 Yakimovsky & Feldman, 1973 A Return to Scene Understanding [Ohta & Kanade 1978]

  41. Images

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