1. Goal: Fast and Robust Velocity Estimation P1P1 P2P2 P3P3 P4P4 Our Approach: Alignment Probability ●Spatial Distance ●Color Distance (if available) ●Probability of Occlusion Annealed Dynamic Histograms Combining 3D Shape, Color, and Motion for Robust Anytime Tracking David Held, Jesse Levinson, Sebastian Thrun, and Silvio Savarese

2. 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 Baseline: Centroid Kalman Filter Local Search Poor Local Optimum! t+1t Baseline: ICP Annealed Dynamic Histograms

3. Goal: Fast and Robust Velocity Estimation P1P1 P2P2 P3P3 P4P4 Our Approach: Alignment Probability ●Spatial Distance ●Color Distance (if available) ●Probability of Occlusion Combining 3D Shape, Color, and Motion for Robust Anytime Tracking David Held, Jesse Levinson, Sebastian Thrun, and Silvio Savarese Annealed Dynamic Histograms

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

5. Laser Data Camera Images System

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

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

8. Velocity Estimation t

9. t+1t

10. Velocity Estimation t+1t

11. Velocity Estimation t+1t

12. Velocity Estimation t+1t

13. ICP Baseline

17. Local Search Poor Local Optimum! ICP Baseline

18. Tracking Probability

19. Velocity Estimation t

20. t+1t

21. Velocity Estimation t+1t

22. Velocity Estimation t+1t

23. Velocity Estimation t+1t

24. Velocity Estimation t+1t

25. Velocity Estimation t+1t XtXt

26. Velocity Estimation t+1t XtXt

27. Measurement Model Motion Model Tracking Probability

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

29. Measurement Model Tracking Probability Motion Model

30. Measurement Model Tracking Probability Motion Model

31. Measurement Model Tracking Probability Motion Model

32. Measurement Model Tracking Probability Motion Model

33. Measurement Model Tracking Probability Motion Model

34. Measurement Model Tracking Probability Motion Model

35. Measurement Model Tracking Probability Motion Model

36. Measurement Model Tracking Probability Motion Model

37. Measurement Model Tracking Probability Motion Model

38. Measurement Model Tracking Probability Motion Model

39. Measurement Model Tracking Probability Motion Model

40. Measurement Model Tracking Probability Motion Model k

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

43. Delta Color Value Probability Color Probability

44. Including Color

49. Delta Color Value Probability

50. Including Color Delta Color Value Probability

51. Including Color Delta Color Value Probability

52. Probabilistic Framework 3D Shape Color Tracking Motion History

53. Tracking Probability P1P1 P2P2 P3P3 P4P4

54. vyvy vxvx ? ? ? ? ?

55. vyvy vxvx

56. Dynamic Decomposition vyvy vxvx

57. vyvy vxvx

58. vyvy vxvx

59. vyvy vxvx Derived from minimizing KL-divergence between approximate distribution and true posterior

60. Annealing Inflate the measurement model

61. Annealing Inflate the measurement model

62. Annealing Inflate the measurement model

63. Algorithm 1.For each hypothesis A.Compute the probability of the alignment Measurement Model Motion Model

64. Algorithm 1.For each hypothesis A.Compute the probability of the alignment B.Finely sample high probability regions Measurement Model Motion Model

65. Algorithm 1.For each hypothesis A.Compute the probability of the alignment B.Finely sample high probability regions C.Go to step 1 to compute the probability of new hypotheses Measurement Model Motion Model

66. Annealing More time More accurate

67. Anytime Tracker

68. Choose runtime based on: Total runtime requirements Importance of tracked object...

69. Comparisons

72. Kalman Filter

73. Kalman Filter ADH Tracker (Ours)

74. Models

75. Quantitative Evaluation 2

76. Sampling Strategies

77. Advantages over Radar

78. Conclusions 3D Shape Color Tracking Motion History ●Robust to Occlusions, Viewpoint Changes

79. Conclusions 3D Shape Color Tracking Motion History ●Robust to Occlusions, Viewpoint Changes ●Runs in Real-time ●Robust to Initialization Errors

81. Delta Color Value Probability Color Probability

82. Error vs Number of Points

83. Error vs Distance

84. Error vs Number of Frames