REAL-TIME DETECTION AND TRACKING

blackeningfourAI and Robotics

Oct 19, 2013 (3 years and 9 months ago)

100 views

REAL
-
TIME DETECTION AND TRACKING
FOR AUGMENTED REALITY ON MOBILE
PHONES

Daniel Wagner, Member, IEEE, Gerhard
Reitmayr
, Member,
IEEE,

Alessandro
Mulloni
, Student Member, IEEE, Tom Drummond,
and

Dieter Schmalstieg, Member, IEEE Computer Society

Outlines


Introduction


Nature Feature Tracking System


FAST detector


Adopted Trackers


Performance & Analysis


Conclusion




Introduction

Reason:


Limited performance on phones

(limited computational resources)

Leads to:


Natural feature tracking not feasible

(Needs long waiting time for large computation)

Goal:


Speed improvement

(enough speed for AR processing & displaying)

Natural Feature Tracking System

1.
SIFT

2.
Ferns (subsets of features)



Both are accurate but not fast enough for phones


Need faster approach


New approaches are called:

1.
PhonySIFT

2.
PhonyFerns

FAST detector

Ref from:

<Machine learning for high
-
speed corner detection>

By Edward
Rosten

and Tom Drummond, University of Cambridge



A corner detector many


times faster than
DoG

but not very robust


to the presence of noise,



Can be trained to be much


faster

Adopted Trackers


PhonySIFT

(Refined from SIFT)


PhonyFerns

(Refined from Ferns)


Patch Tracker (developed by authors)


Combined Tracking


PhonySIFT

+
PatchTracker


PhonyFerns

+
PatchTracker



Ferns

Ref from:

<Fast
Keypoint

Recognition in Ten Lines of Code>

by Mustafa
Özuysal

Pascal
Fua

Vincent
Lepetit
, Computer Vision Laboratory ,Switzerland



An
keypoint

tracker using statistical algorithm

and can be trained to get higher matching rate


As good as SIFT, or even better performance





SIFT to
PhonySIFT


Changes:


Uses FAST corner detector to all scaled images to
detect feature points instead of scale
-
crossing
DoG


Only 3x3
subregions
, 4bins each , creates 36
-
d vector

Ferns to
PhonyFerns


Changes:


Uses FAST detector to increase detection speed


Reduces each ferns size


Uses 8
-
bit size to store probability instead of using 4
bytes float point value


modifying the training scheme to use all FAST responses
within the 8
-
neighborhood



PatchTracker


Both the scene and the camera pose change only
slightly between two successive frames


New feature positions can be successfully predicted by
old one with defined range search.


Speed is less
dependency with
the camera resolution


Combined Tracking

Performance & Analysis


Platform:Asus

P552W (
Cellphone
)


624Mhz CPU


240x320 screen resolution


No float point unit


No 3D acceleration


Platform:Dell

Notebook (PC)


2.5Ghz , limited to use single core


With float point support

Performance & Analysis

Performance & Analysis

Robustness results over different tracking targets

Performance & Analysis

The following graph shows the statistics of above situations

Performance & Analysis

Conclusion


Successfully worked with tracking system on phones


In the future, faster CPUs could come, and the choice
of next generation of tracking technique may be
different, and may enable more expensive per
-
pixel processing

The End

Thank you for listening