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Goal: Fast and Robust Velocity Estimation

Combining 3D Shape, Color, and Motion for Robust Anytime Tracking. David Held, Jesse Levinson, Sebastian Thrun , and Silvio Savarese. Goal: Fast and Robust Velocity Estimation. Our Approach: Alignment Probability. P 1. P 2. Annealed Dynamic Histograms. P 4. P 3. Spatial Distance

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Goal: Fast and Robust Velocity Estimation

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  1. Combining 3D Shape, Color, and Motion for Robust Anytime Tracking David Held, Jesse Levinson, Sebastian Thrun, and Silvio Savarese Goal: Fast and Robust Velocity Estimation Our Approach: Alignment Probability P1 P2 Annealed Dynamic Histograms P4 P3 • Spatial Distance • Color Distance (if available) • Probability of Occlusion

  2. Combining 3D Shape, Color, and Motion for Robust Anytime Tracking David Held, Jesse Levinson, Sebastian Thrun, and Silvio Savarese Goal: Fast and Robust Velocity Estimation t+1 t Baseline: Centroid Kalman Filter Annealed Dynamic Histograms Local Search Poor Local Optimum! Baseline: ICP Actual Motion Occlusions

  3. Combining 3D Shape, Color, and Motion for Robust Anytime Tracking David Held, Jesse Levinson, Sebastian Thrun, and Silvio Savarese Goal: Fast and Robust Velocity Estimation Our Approach: Alignment Probability P1 P2 Annealed Dynamic Histograms P4 P3 • Spatial Distance • Color Distance (if available) • Probability of Occlusion

  4. Motivation Quickly and robustly estimate the speed of nearby objects

  5. System Camera Images Laser Data

  6. System Camera Images Laser Data Previous Work (Teichman, et al)

  7. System Camera Images Velocity Estimation Laser Data This Work Previous Work (Teichman, et al)

  8. Velocity Estimation t

  9. Velocity Estimation t+1 t

  10. Velocity Estimation t+1 t

  11. Velocity Estimation t+1 t

  12. Velocity Estimation t+1 t Actual Motion Occlusions

  13. ICP Baseline

  14. ICP Baseline

  15. ICP Baseline

  16. ICP Baseline

  17. ICP Baseline Local Search Poor Local Optimum!

  18. Tracking Probability

  19. Velocity Estimation t

  20. Velocity Estimation t+1 t

  21. Velocity Estimation t+1 t

  22. Velocity Estimation t+1 t

  23. Velocity Estimation t+1 t

  24. Velocity Estimation t+1 t

  25. Velocity Estimation t+1 t Xt

  26. Velocity Estimation t+1 t Xt

  27. Tracking Probability Measurement Model Motion Model

  28. Tracking Probability Measurement Model Motion Model Constant velocity Kalman filter

  29. Tracking Probability Measurement Model Motion Model

  30. Tracking Probability Measurement Model Motion Model

  31. Tracking Probability Measurement Model Motion Model

  32. Tracking Probability Measurement Model Motion Model

  33. Tracking Probability Measurement Model Motion Model

  34. Tracking Probability Measurement Model Motion Model

  35. Tracking Probability Measurement Model Motion Model

  36. Tracking Probability Measurement Model Motion Model

  37. Tracking Probability Measurement Model Motion Model

  38. Tracking Probability Measurement Model Motion Model

  39. Tracking Probability Measurement Model Motion Model

  40. Tracking Probability Measurement Model Motion Model k

  41. Tracking Probability Measurement Model Motion Model k Sensor noise Sensor resolution

  42. Color Probability Probability Delta Color Value

  43. Including Color

  44. Including Color

  45. Including Color

  46. Including Color

  47. Including Color

  48. Including Color Probability Delta Color Value

  49. Including Color Probability Delta Color Value

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