Multi-Object Tracking with Radar
Transcript of Multi-Object Tracking with Radar
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Multi-Object Tracking with Radar
Karthik Ravindran
Nigam Katta
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Agenda
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1. Introduction to Target tracking
2. Radar sensor and what it measures
3. Kalman filter for single target tracking
4. Generalization to multiple targets
5. Addressing the Data association problem
6. Summary
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Target tracking
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Target tracking is the problem of estimating the kinematic parameters
(position, velocity etc.) of moving targets using sensor measurements
The number of targets can vary from one or to many
The sensor can itself be static or moving
Tracking is essential for environment perception in the context of autonomous
navigation
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Tracking Illustration
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Ego vehicle
sensor
Target 1
Target 2
Target 3
trajectories
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Range-Bearing sensor
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• Measures the radial distance and orientation (azimuth angle, elevation angle) of the target from the
sensor
Examples of Range-bearing sensor
(a) Radar
(b) Lidar
(c) Stereo camera
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What the Radar measures
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• In three dimensions, the sensor measures (a) range (b) azimuth (c) elevation (b) doppler
Radial velocity = doppler
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What the Radar measures
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• Multiple measurements (detections) for each target
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Main components of a Tracker
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Kalman filter
Data association
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Kalman Filter
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Uses the Range-bearing sensor measurements to estimate the positions and velocities of different targets
observed in the field of view of the sensor
Recursively estimates the kinematic parameters (position, velocity) at each time-step based on the sensor
measurements at each time-step and the previous estimates
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Kalman Filter
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Define State vector :
s = ( position, velocity, position, velocity, position, velocity … )
Define Measurement vector :
z = (range,azimuth,elevation,doppler, range,azimuth,elevation,doppler, range,azimuth,elevation,doppler … )
target 1 target 2
target 1
target 3
target 2 target 3
(position X, position Y, position Z)
(velocity X, velocity Y, velocity Z)
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Measurement model
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what we are interested in
what the sensor measures
( Measurement noise covariance matrix )
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State transition model
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( Process noise covariance matrix )
vehicle vehicle
Position 1 Position 2
(predicted)
Velocity V
Time T
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Prediction-Correction steps
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vehicle vehicle
Position 1 Position 2
(predicted)
Velocity V
Time T
vehicle
Position 2
(estimated)
vehicle
Position 2
(Radar observation)
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Multiple measurements per target
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Apply Kalman filter for each measurement (detection) and linearly combine the
individual estimates
.
.
.
.
{Detection 1}
{Detection 2}
{Detection n}
Kalman Filter 1
Kalman Filter2
Kalman Filter n
∑
p1
p2
pn
Estimated vehicle state
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Tracking multiple targets
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• Need to identify the number of targets – Clustering
• Each cluster represents a target
• Once clustered, for every scan the detections need to be mapped
to these targets – Data association
• Every time a new target comes into the FOV of the sensor, a new
cluster is created
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Data association
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• Map the detections to different targets
• Compute an association probability each target-detection pair
target
detection
d1
d2d3
d4
d5
d6
d7
d8
Target 1
Target 2
Target 3
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Clutter
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• Spurious detections from static targets (Road, Clouds, Sea etc.)
target
detection from target
Clutter detection
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Data association methods
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• Nearest neighbourhood method
• Probabilistic data association
• Joint probabilistic data association
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Nearest Neighbourhood method
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• Detections are mapped to its nearest target
• Does not discriminate clutter
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Probabilistic data association
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• Assumes a single target in the FOV
• Assumes a probabilistic model for the spatial distribution of clutter
• Computes an association probability for each detection
• Detections probable of being a clutter will assume smaller association probabilities
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Joint probabilistic data association
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• Assumes multiple targets in the FOV
• Assumes a probabilistic model for the spatial distribution of clutter
• Computes an association probability for each detection-target pair
• Detections probable of being a clutter will assume smaller association probabilities
p1p2
p3
p4p5
p6
p7p8
p9
p1, p2, p3 ….p9are theassociationprobabilitiesconsidering theother objects inthe scenario.
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Summary
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• Defined the Target tracking problem
• Radar sensor and its measurements (detections)
• Kalman filter for state estimation
• Generalization to multiple detections, multiple targets
• Clutter detections
• Data association methods
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