laser based tracking of mutually occluding dynamic objects
DESCRIPTION
University of Aveiro 2010 Department of Mechanical Engineering. Laser based tracking of mutually occluding dynamic objects. Jorge Almeida. 10 September 2010. Overview. Overview. Objectives Motivation Laser Algorithm Experiments Results Conclusions. Objectives. Objectives. - PowerPoint PPT PresentationTRANSCRIPT
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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