Goal: Fast and Robust Velocity Estimation P1P1 P2P2 P3P3 P4P4 Our Approach: Alignment Probability ●Spatial Distance ●Color Distance (if available) ●Probability.

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Presentation transcript:

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

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

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

Motivation Quickly and robustly estimate the speed of nearby objects

Laser Data Camera Images System

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

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

Velocity Estimation t

t+1t

Velocity Estimation t+1t

Velocity Estimation t+1t

Velocity Estimation t+1t

ICP Baseline

Local Search Poor Local Optimum! ICP Baseline

Tracking Probability

Velocity Estimation t

t+1t

Velocity Estimation t+1t

Velocity Estimation t+1t

Velocity Estimation t+1t

Velocity Estimation t+1t

Velocity Estimation t+1t XtXt

Velocity Estimation t+1t XtXt

Measurement Model Motion Model Tracking Probability

Measurement Model Motion Model Tracking Probability Constant velocity Kalman filter

Measurement Model Tracking Probability Motion Model

Measurement Model Tracking Probability Motion Model

Measurement Model Tracking Probability Motion Model

Measurement Model Tracking Probability Motion Model

Measurement Model Tracking Probability Motion Model

Measurement Model Tracking Probability Motion Model

Measurement Model Tracking Probability Motion Model

Measurement Model Tracking Probability Motion Model

Measurement Model Tracking Probability Motion Model

Measurement Model Tracking Probability Motion Model

Measurement Model Tracking Probability Motion Model

Measurement Model Tracking Probability Motion Model k

Measurement Model Tracking Probability Motion Model Sensor noise Sensor resolution k

Delta Color Value Probability Color Probability

Including Color

Delta Color Value Probability

Including Color Delta Color Value Probability

Including Color Delta Color Value Probability

Probabilistic Framework 3D Shape Color Tracking Motion History

Tracking Probability P1P1 P2P2 P3P3 P4P4

vyvy vxvx ? ? ? ? ?

vyvy vxvx

Dynamic Decomposition vyvy vxvx

vyvy vxvx

vyvy vxvx

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

Annealing Inflate the measurement model

Annealing Inflate the measurement model

Annealing Inflate the measurement model

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

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

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

Annealing More time More accurate

Anytime Tracker

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

Comparisons

Kalman Filter

Kalman Filter ADH Tracker (Ours)

Models

Quantitative Evaluation 2

Sampling Strategies

Advantages over Radar

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

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

Delta Color Value Probability Color Probability

Error vs Number of Points

Error vs Distance

Error vs Number of Frames