Jorge Almeida

Laser based tracking of mutually occluding dynamic

objects

University of Aveiro 2010Department of Mechanical Engineering

10 September 2010

• Objectives• Motivation• Laser• Algorithm• Experiments• Results• Conclusions

OVERVIEW

Overview

• Develop an algorithm capable of following multiple targets– Overcoming temporary occlusions– Obtain position and velocity of targets

• Laser rangefinder

Objectives

OBJECTIVES

• Obtain a dynamic perception of the vicinity

• Indoors– Building security– Optimization of motion paths

• Outdoors– Driver assistance systems– Advanced path planning

Motivation

INTRODUCTION

• 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

Typical scan

LASER – SCAN

Laser

Typical scan

LASER – SCAN

Columns

Wall

Laser

Typical scan

LASER – SCAN

Pedestrians

Laser

• Two main phases– Object reconstruction

• Preprocessing• Segmentation• Data reduction

– Object association• Motion prediction

Tracking algorithm

ALGORITHM

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Increase performance

• Single associations

• Exclusion zones– ezA

• Prevents the tracking of objects’ fragments

– ezB• Avoids wrong associations

Heuristic rules

DATA ASSOCIATION – HEURISTIC RULES

• 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

• 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

RESULTS– REAL WORLD SCENARIO

Real world scenario

• 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

Close proximity objects

RESULTS – CLOSE PROXUMITY OBJECTS

• 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

Demonstration

Jorge Almeida

Laser based tracking of mutually occluding dynamic

objects

University of Aveiro 2010Department of Mechanical Engineering

10 September 2010