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Jorge Almeida
Laser based tracking of mutually occluding dynamic
objects
University of Aveiro 2010Department of Mechanical Engineering
10 September 2010
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• Objectives• Motivation• Laser• Algorithm• Experiments• Results• Conclusions
OVERVIEW
Overview
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• Develop an algorithm capable of following multiple targets– Overcoming temporary occlusions– Obtain position and velocity of targets
• Laser rangefinder
Objectives
OBJECTIVES
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• Obtain a dynamic perception of the vicinity
• Indoors– Building security– Optimization of motion paths
• Outdoors– Driver assistance systems– Advanced path planning
Motivation
INTRODUCTION
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• 2D Laser rangefinder
• Hokuyo UTM-30LX– 30 m max range– 40 Hz scan frequency– 0.25° angular resolution– 270° field of view
• Direct measurement of distance to targets
Laser
LASER
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Typical scan
LASER – SCAN
Laser
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Typical scan
LASER – SCAN
Columns
Wall
Laser
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Typical scan
LASER – SCAN
Pedestrians
Laser
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• Two main phases– Object reconstruction
• Preprocessing• Segmentation• Data reduction
– Object association• Motion prediction
Tracking algorithm
ALGORITHM
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• Remove noise
• Moving average filter– Applied to the data in polar coordinates (r, θ)
• The filter is limited in order not to compromise the responsiveness
• Obtain the Cartesian coordinates (x, y)
Preprocessing
OBJECT CREATION – PREPROCESSING
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• Clustering of measurements belonging to the same object
• Several steps– Occluded points detection– Clustering of visible and
occluded points
• Euclidian distance betweenconsecutive points
Segmentation
OBJECT CREATION – SEGMENTATION
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• Simplify the data handling
• Conversion from groups of points to lines– This representation is enough for all intended
purposes
• Iterative End-Point Fit (IEPF)
Data reduction
OBJECT CREATION – DATA REDUCTION
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• Search zones– Shaped as ellipses
• New objects are added to thetracking list
• Not associated objects are removed from the list
• Association aided by– Motion prediction– Heuristic rules
Data association
DATA ASSOCIATION
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• Search zones– Shaped as ellipses
• New objects are added to thetracking list
• Not associated objects are removed from the list
• Association aided by– Motion prediction– Heuristic rules
Data association
DATA ASSOCIATION
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• Centered at the object predicted position
• Aligned with the velocityvector
• Variable axes lengths– Object size– Occlusion time– Prediction errors
Search zone
DATA ASSOCIATION – SEARCH ZONE
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• Adaptive linear Kalman filters– Two filters per object
• Constant velocity motion models
• Process noise covariance is coupled with the prediction error
Motion prediction
DATA ASSOCIATION – MOTION PREDICTION
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• Increase performance
• Single associations
• Exclusion zones– ezA
• Prevents the tracking of objects’ fragments
– ezB• Avoids wrong associations
Heuristic rules
DATA ASSOCIATION – HEURISTIC RULES
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• Robustness to occlusion in real world scenario– Outdoors people pathway– Global performance test
• Tracking of nearby moving objects– Person moving close to a wall– Security applications
Experiments
EXPERIMENTS
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• Long duration trial (~17 min) in a very crowded environment
• Ground-truth obtained with a video camera
• Performance evaluation– Percentage tracking time– Percentage of targets with tracking faults
• Loss of a target• Id switch• Fake tracks creation
Real world scenario
RESULTS– REAL WORLD SCENARIO
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RESULTS– REAL WORLD SCENARIO
Real world scenario
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• Two distinct target types, single target (A) and multiple target (B)
• Good results
• Type B targets present worst results– Long occlusions
• Most common fault was target lost
Real world scenario
RESULTS– REAL WORLD SCENARIO
Type Number of targets
% time tracked % objects with tracking faults
A 37 98.5 5.4
B 26 89.9 19.2
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Close proximity objects
RESULTS – CLOSE PROXUMITY OBJECTS
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• An algorithm capable of tracking multiple targets using laser data was developed.
• The algorithm was shown robust and effective even under extensive occlusion.
• The Kalman filter was an effective tool in the prediction of objects motion.
Conclusions
CONCLUSIONS
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Demonstration
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Jorge Almeida
Laser based tracking of mutually occluding dynamic
objects
University of Aveiro 2010Department of Mechanical Engineering
10 September 2010