Kanade-Lucas-Tomasi (KLT) Tracker
Transcript of Kanade-Lucas-Tomasi (KLT) Tracker
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Kanade-Lucas-Tomasi (KLT) Tracker16-385 Computer Vision (Kris Kitani)
Carnegie Mellon University
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https://www.youtube.com/watch?v=rwIjkECpY0M
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Feature-based tracking
How should we select features?
How should we track them from frame to frame?
Up to now, we’ve been aligning entire images but we can also track just small image regions too!
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An Iterative Image Registration Technique with an Application to Stereo Vision.
1981
Lucas Kanade
Detection and Tracking of Feature Points.
1991
Kanade Tomasi
Good Features to Track.
1994
Tomasi Shi
History of the
Kanade-Lucas-Tomasi (KLT) Tracker
The original KLT algorithm
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Method for aligning (tracking) an image patch
Kanade-Lucas-Tomasi
Method for choosing the best feature (image patch)
for tracking
Lucas-Kanade Tomasi-KanadeHow should we select features?How should we track them from frame
to frame?
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What are good features for tracking?
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What are good features for tracking?
Intuitively, we want to avoid smooth regions and edges. But is there a more
is principled way to define good features?
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Can be derived from the tracking algorithm
What are good features for tracking?
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Can be derived from the tracking algorithm
What are good features for tracking?
‘A feature is good if it can be tracked well’
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Recall the Lucas-Kanade image alignment method:X
x
[I(W(x;p))� T (x)]2
X
x
[I(W(x;p+�p))� T (x)]2incremental update
error function (SSD)
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Recall the Lucas-Kanade image alignment method:X
x
[I(W(x;p))� T (x)]2
X
x
[I(W(x;p+�p))� T (x)]2incremental update
error function (SSD)
X
x
I(W(x;p)) +rI
@W
@p�p� T (x)
�2linearize
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Recall the Lucas-Kanade image alignment method:X
x
[I(W(x;p))� T (x)]2
X
x
[I(W(x;p+�p))� T (x)]2incremental update
error function (SSD)
X
x
I(W(x;p)) +rI
@W
@p�p� T (x)
�2linearize
H =X
x
rI
@W
@p
�> rI
@W
@p
�
�p = H�1X
x
rI
@W
@p
�>[T (x)� I(W(x;p))]
Gradient update
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Recall the Lucas-Kanade image alignment method:X
x
[I(W(x;p))� T (x)]2
X
x
[I(W(x;p+�p))� T (x)]2incremental update
error function (SSD)
X
x
I(W(x;p)) +rI
@W
@p�p� T (x)
�2linearize
H =X
x
rI
@W
@p
�> rI
@W
@p
�
�p = H�1X
x
rI
@W
@p
�>[T (x)� I(W(x;p))]
Gradient update
Update p p+�p
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Stability of gradient decent iterations depends on …
�p = H�1X
x
rI
@W
@p
�>[T (x)� I(W(x;p))]
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Stability of gradient decent iterations depends on …
H =X
x
rI
@W
@p
�> rI
@W
@p
�
�p = H�1X
x
rI
@W
@p
�>[T (x)� I(W(x;p))]
Inverting the Hessian
When does the inversion fail?
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Stability of gradient decent iterations depends on …
H =X
x
rI
@W
@p
�> rI
@W
@p
�
�p = H�1X
x
rI
@W
@p
�>[T (x)� I(W(x;p))]
Inverting the Hessian
When does the inversion fail?
H is singular. But what does that mean?
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Above the noise level
�1 � 0
�2 � 0
Well-conditioned
both Eigenvalues are large
both Eigenvalues have similar magnitude
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Concrete example: Consider translation model
W(x;p) =
x+ p1
y + p2
�W
@p=
1 00 1
�
H =X
x
rI
@W
@p
�> rI
@W
@p
�
=X
x
1 00 1
� Ix
Iy
� ⇥Ix
Iy
⇤ 1 00 1
�
=
Px
Ix
Ix
Px
Iy
IxP
x
Ix
Iy
Px
Iy
Iy
�
Hessian
How are the eigenvalues related to image content?
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interpreting eigenvalues
λ1
λ2
λ2 >> λ1
λ1 >> λ2�1 ⇠ 0
�2 ⇠ 0
What kind of image patch does each region represent?
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interpreting eigenvalueshorizontal edge
vertical edge
flat
corner
λ1
λ2
λ2 >> λ1
λ1 >> λ2
λ1 ~ λ2
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interpreting eigenvalueshorizontal edge
vertical edge
flat
corner
λ1
λ2
λ2 >> λ1
λ1 >> λ2
λ1 ~ λ2
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What are good features for tracking?
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What are good features for tracking?
min(�1,�2) > �
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KLT algorithm1. Find corners satisfying
2. For each corner compute displacement to next frame using the Lucas-Kanade method
3. Store displacement of each corner, update corner position
4. (optional) Add more corner points every M frames using 1
5. Repeat 2 to 3 (4)
6. Returns long trajectories for each corner point
min(�1,�2) > �