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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

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Delta Color Value Probability Color Probability

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Error vs Number of Points

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Error vs Distance

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Error vs Number of Frames

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