Goal: Fast and Robust Velocity Estimation P 1 P 2 P 3 P 4 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
Transcript
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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
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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 Baseline: Centroid
Kalman Filter Local Search Poor Local Optimum! t+1t Baseline: ICP
Annealed Dynamic Histograms
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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
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Motivation Quickly and robustly estimate the speed of nearby
objects
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Laser Data Camera Images System
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Laser Data Camera Images System Previous Work (Teichman, et
al)
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System Laser Data Camera Images This Work Velocity Estimation
Previous Work (Teichman, et al)
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Velocity Estimation t
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t+1t
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Velocity Estimation t+1t
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Velocity Estimation t+1t
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Velocity Estimation t+1t
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ICP Baseline
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Local Search Poor Local Optimum! ICP Baseline
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Tracking Probability
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Velocity Estimation t
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t+1t
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Velocity Estimation t+1t
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Velocity Estimation t+1t
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Velocity Estimation t+1t
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Velocity Estimation t+1t
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Velocity Estimation t+1t XtXt
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Velocity Estimation t+1t XtXt
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Measurement Model Motion Model Tracking Probability
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Measurement Model Motion Model Tracking Probability Constant
velocity Kalman filter
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Measurement Model Tracking Probability Motion Model
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Measurement Model Tracking Probability Motion Model
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Measurement Model Tracking Probability Motion Model
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Measurement Model Tracking Probability Motion Model
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Measurement Model Tracking Probability Motion Model
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Measurement Model Tracking Probability Motion Model
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Measurement Model Tracking Probability Motion Model
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Measurement Model Tracking Probability Motion Model
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Measurement Model Tracking Probability Motion Model
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Measurement Model Tracking Probability Motion Model
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Measurement Model Tracking Probability Motion Model
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Measurement Model Tracking Probability Motion Model k
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Measurement Model Tracking Probability Motion Model Sensor
noise Sensor resolution k
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Delta Color Value Probability Color Probability
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Including Color
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Delta Color Value Probability
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Including Color Delta Color Value Probability
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Including Color Delta Color Value Probability
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Probabilistic Framework 3D Shape Color Tracking Motion
History
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Tracking Probability P1P1 P2P2 P3P3 P4P4
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vyvy vxvx ? ? ? ? ?
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vyvy vxvx
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Dynamic Decomposition vyvy vxvx
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vyvy vxvx
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vyvy vxvx
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vyvy vxvx Derived from minimizing KL-divergence between
approximate distribution and true posterior
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Annealing Inflate the measurement model
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Annealing Inflate the measurement model
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Annealing Inflate the measurement model
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Algorithm 1.For each hypothesis A.Compute the probability of
the alignment Measurement Model Motion Model
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Algorithm 1.For each hypothesis A.Compute the probability of
the alignment B.Finely sample high probability regions Measurement
Model Motion Model
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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
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Annealing More time More accurate
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Anytime Tracker
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Choose runtime based on: Total runtime requirements Importance
of tracked object...
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Comparisons
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Kalman Filter
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Kalman Filter ADH Tracker (Ours)
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Models
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Quantitative Evaluation 2
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Sampling Strategies
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Advantages over Radar
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Conclusions 3D Shape Color Tracking Motion History Robust to
Occlusions, Viewpoint Changes
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Conclusions 3D Shape Color Tracking Motion History Robust to
Occlusions, Viewpoint Changes Runs in Real-time Robust to
Initialization Errors