Deep Tracking & Flow Instructor - Simon Lucey 16-623 - - - PowerPoint PPT Presentation

Deep Tracking & Flow

Instructor - Simon Lucey

16-623 - Designing Computer Vision Apps

Today

• Deep Features
• Deep Tracking
• Deep Flow

Primary Visual Cortex

            

Spatial Sensitivity

• Which image has the greatest distortion with respect to the

template?

Kingdom, Field, Olmos, 2007

Spatial Sensitivity

Kingdom, Field, Olmos, 2007

Handling Geometric Distortion

• As pointed out in seminal work by Berg and Malik

(CVPR’01) the effectiveness of SSD will degrade with significant viewpoint change.

• Two options to match local image patches:-

6

“1D Patch” “Distorted 1D Patch”

Source: A. C. Berg

Handling Geometric Distortion

• As pointed out in seminal work by Berg and Malik

(CVPR’01) the effectiveness of SSD will degrade with significant viewpoint change.

• Two options to match local image patches:-

6

“1D Patch” “Distorted 1D Patch”

Source: A. C. Berg

“match”

Handling Geometric Distortion

• As pointed out in seminal work by Berg and Malik

(CVPR’01) the effectiveness of SSD will degrade with significant viewpoint change.

• Two options to match local image patches:-
• 1. simultaneously estimate the distortion and position of matching patch

7

“1D Patch” “Distorted 1D Patch”

Handling Geometric Distortion

• As pointed out in seminal work by Berg and Malik

(CVPR’01) the effectiveness of SSD will degrade with significant viewpoint change.

• Two options to match local image patches:-
• 1. simultaneously estimate the distortion and position of matching patch

7

“1D Patch” “Distorted 1D Patch” “align”

Handling Geometric Distortion

• As pointed out in seminal work by Berg and Malik

(CVPR’01) the effectiveness of SSD will degrade with significant viewpoint change.

• Two options to match local image patches:-
• 1. simultaneously estimate the distortion and position of matching patch

7

“1D Patch” “Distorted 1D Patch” “align”

Handling Geometric Distortion

• As pointed out in seminal work by Berg and Malik

(CVPR’01) the effectiveness of SSD will degrade with significant viewpoint change.

• Two options to match local image patches:-
• 1. simultaneously estimate the distortion and position of matching patch

7

“1D Patch” “Distorted 1D Patch” “align” “match”

Handling Geometric Distortion

• As pointed out in seminal work by Berg and Malik

(CVPR’01) the effectiveness of SSD will degrade with significant viewpoint and/or illumination change.

• Two options to match patches:-
• 1. simultaneously estimate the distortion and position of matching patch.
• 2. to “blur” the template window performing matching coarse-to-fine.

8

“1D Patch” “Distorted 1D Patch”

Handling Geometric Distortion

• As pointed out in seminal work by Berg and Malik

(CVPR’01) the effectiveness of SSD will degrade with significant viewpoint and/or illumination change.

• Two options to match patches:-
• 1. simultaneously estimate the distortion and position of matching patch.
• 2. to “blur” the template window performing matching coarse-to-fine.

8

“1D Patch” “Distorted 1D Patch” “blur”

Handling Geometric Distortion

• As pointed out in seminal work by Berg and Malik

(CVPR’01) the effectiveness of SSD will degrade with significant viewpoint and/or illumination change.

• Two options to match patches:-
• 1. simultaneously estimate the distortion and position of matching patch.
• 2. to “blur” the template window performing matching coarse-to-fine.

8

“1D Patch” “Distorted 1D Patch” “blur” “match”

Handling Geometric Distortion

• As pointed out in seminal work by Berg and Malik

(CVPR’01) the effectiveness of SSD will degrade with significant viewpoint and/or illumination change.

• Two options to match patches:-
• 1. simultaneously estimate the distortion and position of matching patch.
• 2. to “blur” the template window performing matching coarse-to-fine.

8

“1D Patch” “Distorted 1D Patch” “blur” “match”

Option 2 is attractive, low computational cost!

Sparseness and Positiveness

• Blurring only works if the signals being matched are

sparse and positive.

• Unfortunately natural images are neither.
• Combination of oriented filter banks and rectification can

remedy this problem with little loss in performance.

9

...

... ... ... ... ...

) ⇤

“Rectification” e.g., oriented gradients, Gabor filters e.g., sigmoid, squared, relu, etc.

Sparseness and Positiveness

• Blurring only works if the signals being matched are

sparse and positive.

• Unfortunately natural images are neither.
• Combination of oriented filter banks and rectification can

remedy this problem with little loss in performance.

10

Reminder: Convolution

8 4 6 2 7

∗

1 2

x

h

“signal” “filter” “convolution

• perator”

Reminder: Convolution

8 4 6 2 7

∗

1 2

x

h

“signal” “filter” “convolution

• perator”

>> conv(x,h,’valid’) ans = 20 14 14 11

Multi-Channel Convolution

∗

y

“single-channel response”

x

“multi-channel signal” “multi-channel filter”

h

Multi-Channel Convolution

∗

y

“single-channel response”

x

“multi-channel signal” “multi-channel filter”

h

K-channels K-channels

Multi-Channel Convolution

y =

K

X

k=1

x(k) ∗ h(k)

Multi-Channel Convolution

∗

y

“multi-channel response”

x

“multi-channel signal” “multi-channel filter”

h

Multi-Channel Convolution

∗

y

“multi-channel response”

x

“multi-channel signal” “multi-channel filter”

h

L-channels

Multi-Channel Convolution

y(l) =

K

X

k=1

x(k) ∗ h(k,l) for l = 1 : L

CNNs for Object Detection

CNNs for Object Detection

image patch 3@ (224x224)

CNNs for Object Detection

image patch 3@ (224x224)

conv L@ (NxM)

L-channels M-pixels N-pixels

D · η ( K X

k=1

x(k) ∗ h(k,l) )

CNNs for Object Detection

image patch 3@ (224x224)

conv L@ (NxM)

η{} → non-linear function (relu, max pooling)

ReLU - Sparse and Positive

• Rectified Linear Unit

relu{x} = max(0, x)

• Connection to LASSO and sparsity??

||y − Ax||2

2 + λ

2 ||x||1

Max Pooling - Down Sampling

Input image Convolutional layer Sub-sampling layer

LeCun 1980

Max Pool Convolutional Layer Input Image max( )

Hierarchical Learning

View-tuned cells Complex Simple

Bob Crimi

Hierarchical Learning

View-tuned cells Complex Simple

Bob Crimi

V1

V2/V4

IT

Ventral Visual Stream

Current State of the Art

image patch 3@ (227x227)

conv1 96@ (55x55) conv2 256@ (27x27) conv3 384@ (13x13) conv4 384@ (13x13) conv5 256@ (13x13) fc-6 (4096) fc-7 (K)

• A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.

“car” “bird” “cat”

. . .

Current State of the Art

image patch 3@ (227x227)

conv1 96@ (55x55) conv2 256@ (27x27) conv3 384@ (13x13) conv4 384@ (13x13) conv5 256@ (13x13) fc-6 (4096) fc-7 (K)

• A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.

“car” “bird” “cat”

. . .

Current State of the Art

image patch 3@ (227x227)

conv1 96@ (55x55) conv2 256@ (27x27) conv3 384@ (13x13) conv4 384@ (13x13) conv5 256@ (13x13) fc-6 (4096) fc-7 (K)

• A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.

     1 . . .     

K × 1

Current State of the Art - Pose Selection

image patch 3@ (224x224)

fc-8 conv1 64@ (54x54) conv2 256@ (27x27) conv3 384@ (13x13) conv4 384@ (13x13) conv5 256@ (13x13) fc-6 (4096) fc-7 (4096)

“car” “bird” “cat”

. . .

• K. Chatfield, V. Lempitsky, A. Vedaldi and A. Zisserman. “Return of the Devil in the Details: Delving Deep into Convolutional Networks.”

In BMVC, 2014.

• A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.

Impact on Object Recognition

ImageNet Challenge Year

BC

(before ConvNets)

AD

(after deep learning)

6.8%

Visualizing CNNs

CNNs as Feature Extraction

image patch 3@ (224x224)

conv1 96@ (54x54) conv2 256@ (27x27) conv3 384@ (13x13) conv4 384@ (13x13) conv5 256@ (13x13)

• K. Chatfield, V. Lempitsky, A. Vedaldi and A. Zisserman. “Return of the Devil in the Details: Delving Deep into Convolutional Networks.”

In BMVC, 2014.

parameters to learn pre-learned parameters (VGG)

fc-8 fc-6 (4096) fc-7 (4096)

• A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.

?

Today

• Deep Features
• Deep Tracking
• Deep Flow

Drawback to Conventional Methods

• Most methods for object tracking employ “online” learning.
• Online methods are expensive, have to make simplifying

assumptions (e.g. circulant Toeplitz) to make things efficient.

∗

x

y

“known signal” “known response” “unknown filter”

h

Deep Tracking Methods

• Recently, there have been works that have tried to explore

the employment of tracking using deep learning features.

• As efficiency is key, strategy is to learn from a large

ensemble of labeled offline videos.

• Of particular interest are two papers,
• 1. D. Held, S. Thrun, and S. Savarese “Learning to Track at 100 FPS

with Deep Regression Networks”, ECCV 2016.

• 2. L. Bertinetto J. Valmadre J. F. Henriques, A. Vedaldi, P. H. S. Torr

“Fully-Convolutional Siamese Networks for Object Tracking”, ArXiv 2016.

conv1 96@ (54x54) conv2 256@ (27x27) conv3 384@ (13x13) conv4 384@ (13x13) conv5 256@ (13x13)

Deep Regression Networks

Previous frame Current frame Predicted loca3on

• f target

within search region Crop Crop What to track Search Region Conv Layers Conv Layers Fully-Connected Layers

• D. Held, S. Thrun, and S. Savarese “Learning to Track at 100 FPS with Deep Regression Networks”, ECCV 2016.

Deep Regression Networks

Previous frame Current frame Predicted loca3on

• f target

within search region Crop Crop What to track Search Region Conv Layers Conv Layers Fully-Connected Layers

• D. Held, S. Thrun, and S. Savarese “Learning to Track at 100 FPS with Deep Regression Networks”, ECCV 2016.

What does this method remind you of?

Deep Regression Networks

Previous frame Current frame Predicted loca3on

• f target

within search region Crop Crop What to track Search Region Conv Layers Conv Layers Fully-Connected Layers

• D. Held, S. Thrun, and S. Savarese “Learning to Track at 100 FPS with Deep Regression Networks”, ECCV 2016.

What does this method remind you of? Why is it fast?

Supervised Descent Method (SDM)

• SDM assumes a linear relationship between appearance and

geometry:

… …

∆p = R[I(p) − T (0)]

• Iteratively updates until convergence
• However, is iteration specific.

R(k)

Xiong & De la Torre 2013

Supervised Descent Method (SDM)

• SDM assumes a linear relationship between appearance and

geometry:

… …

∆p = R[I(p) − T (0)]

• Iteratively updates until convergence
• However, is iteration specific.

R(k)

Xiong & De la Torre 2013

What is a potential issue here?

Deep Regression Networks

• D. Held, S. Thrun, and S. Savarese “Learning to Track at 100 FPS with Deep Regression Networks”, ECCV 2016.

Previous)) video)frame) centered)on))

• bject)

Current)video)frame,)) shi6ed,)with)) ground9truth) bounding)box)

Deep Regression Networks

• D. Held, S. Thrun, and S. Savarese “Learning to Track at 100 FPS with Deep Regression Networks”, ECCV 2016.

Image& centered&on&&

• bject&

Shi2ed&image& with&ground5truth& bounding&box&

Deep Regression Networks

Accuracy Rank Robustness Rank

GOTURN (Ours)

• D. Held, S. Thrun, and S. Savarese “Learning to Track at 100 FPS with Deep Regression Networks”, ECCV 2016.

How does it work?

• Two hypotheses,
• 1. The network compares the previous frame to the current frame to find

the target object in the current frame.

• 2. The network acts as a local generic “object detector” and simply

locates the nearest “object.” 


• D. Held, S. Thrun, and S. Savarese “Learning to Track at 100 FPS with Deep Regression Networks”, ECCV 2016.

None Illumination change Camera motion Motion change Size change Occlusion 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Overall Errors

None Illumina,on Change Camera Mo,on Mo,on Change Occlusion Size Change

How does it work?

• Two hypotheses,
• 1. The network compares the previous frame to the current frame to find

the target object in the current frame.

• 2. The network acts as a local generic “object detector” and simply

locates the nearest “object.” 


• D. Held, S. Thrun, and S. Savarese “Learning to Track at 100 FPS with Deep Regression Networks”, ECCV 2016.

Generality vs. Specificity

50 100 150 200 250 300 0.05 0.1 0.15 0.2 0.25 0.3 0.35 Number of training videos Tracking Errors Same object class in training and test sets Different object class in training and test sets

Overall Errors

• D. Held, S. Thrun, and S. Savarese “Learning to Track at 100 FPS with Deep Regression Networks”, ECCV 2016.

Fully Convolutional Siamese Networks

127x127x3 6x6x128 255x255x3 22x22x128 17x17x1

• L. Bertinetto J. Valmadre J. F. Henriques, A. Vedaldi, P. H. S. Torr “Fully-Convolutional Siamese Networks for Object Tracking”, ArXiv 2016.

Fully Convolutional Siamese Networks

127x127x3 6x6x128 255x255x3 22x22x128 17x17x1

• L. Bertinetto J. Valmadre J. F. Henriques, A. Vedaldi, P. H. S. Torr “Fully-Convolutional Siamese Networks for Object Tracking”, ArXiv 2016.

“Siamese” as they apply an identical transformation to both inputs “Template” “Source Image”

Fully Convolutional

image patch 3@ (224x224)

conv1 96@ (54x54) conv2 256@ (27x27) conv3 384@ (13x13) conv4 384@ (13x13) conv5 256@ (13x13)

?

• L. Bertinetto J. Valmadre J. F. Henriques, A. Vedaldi, P. H. S. Torr “Fully-Convolutional Siamese Networks for Object Tracking”, ArXiv 2016.

Fully Convolutional

image patch 3@ (224x224)

?

• L. Bertinetto J. Valmadre J. F. Henriques, A. Vedaldi, P. H. S. Torr “Fully-Convolutional Siamese Networks for Object Tracking”, ArXiv 2016.

Fully Convolutional

image patch 3@ (224x224)

?

ϕ{ei ∗ x} = ei ∗ ϕ{x}

ei = [0, . . . , 1, . . . , 0]T

i-th index

• L. Bertinetto J. Valmadre J. F. Henriques, A. Vedaldi, P. H. S. Torr “Fully-Convolutional Siamese Networks for Object Tracking”, ArXiv 2016.

Fully Convolutional Siamese Networks

• L. Bertinetto J. Valmadre J. F. Henriques, A. Vedaldi, P. H. S. Torr “Fully-Convolutional Siamese Networks for Object Tracking”, ArXiv 2016.
• Example training sequences.

Fully Convolutional Siamese Networks

Overlap threshold

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Success rate

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Success plots of OPE

SiamFC (ours) [0.612] LCT (2015) [0.612] SiamFC_3s (ours) [0.608] CCT (2015) [0.605] Staple (2016) [0.600] SCT4 (2016) [0.595] KCFDP (2015) [0.581] DSST (2014) [0.554] DLSSVM_NU (2016) [0.550] SCM (2012) [0.499]

• L. Bertinetto J. Valmadre J. F. Henriques, A. Vedaldi, P. H. S. Torr “Fully-Convolutional Siamese Networks for Object Tracking”, ArXiv 2016.

Fully Convolutional Siamese Networks

Robustness (S = 30.00)

Accuracy

AR plot for experiment baseline (mean)

SiamFC (ours) SiamFC_3s (ours) Staple GOTURN ACAT DGT DSST eASMS HMMTxD KCF MCT PLT_13 PLT_14 SAMF

• L. Bertinetto J. Valmadre J. F. Henriques, A. Vedaldi, P. H. S. Torr “Fully-Convolutional Siamese Networks for Object Tracking”, ArXiv 2016.

Fully Convolutional Siamese Networks

• L. Bertinetto J. Valmadre J. F. Henriques, A. Vedaldi, P. H. S. Torr “Fully-Convolutional Siamese Networks for Object Tracking”, ArXiv 2016.

Frame 1 (init.) Frame 50 Frame 100 Frame 200

Fully Convolutional Siamese Networks

• L. Bertinetto J. Valmadre J. F. Henriques, A. Vedaldi, P. H. S. Torr “Fully-Convolutional Siamese Networks for Object Tracking”, ArXiv 2016.

Frame 1 (init.) Frame 50 Frame 100 Frame 200

Frame 1 (init.) Frame 50 Frame 100 Frame 200

Today

• Deep Features
• Deep Tracking
• Deep Flow

Flow = Parts Based Registration

min

x N

X

i=1

Di(xi) + λ R(x)

Flow = Parts Based Registration

min

x N

X

i=1

Di(xi) + λ R(x) Does the image at look like the part?

xi

ith

Flow = Parts Based Registration

min

x N

X

i=1

Di(xi) + λ R(x) Does the image at look like the part?

xi

ith

Do the joint locations of the parts match the object?

Reminder - Exhaustive Search

“We can do much better than this if the graph is sparse.”

p1 p2 p3 p4 p5 O(M N)

Reminder - Exhaustive Search

“We can do much better than this if the graph is sparse.”

p1 p2 p3 p4 p5 O(NM 2)

Learning

{Di(xi)}N

i=1

for every pixel u

patch at pixel u (positive) all other patches (negatives)

• H. Bristow, J. Valmadre, and S. Lucey “Dense Semantic Correspondence where Every Pixel is a Classifier”, ICCV 2015.

Learning

{Di(xi)}N

i=1

• J. Zˇbontar & Y. LeCunn “Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches”, JMLR 2015.

Concatenate Fully-connected, ReLU Fully-connected, ReLU Fully-connected, ReLU Fully-connected, Sigmoid Righ input patch Convolution, ReLU Convolution, ReLU Convolution, ReLU Left input patch Convolution, ReLU Convolution, ReLU Convolution, ReLU Similarity score

Learning

{Di(xi)}N

i=1

• J. Zˇbontar & Y. LeCunn “Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches”, JMLR 2015.

“Siamese” network

Concatenate Fully-connected, ReLU Fully-connected, ReLU Fully-connected, ReLU Fully-connected, Sigmoid Righ input patch Convolution, ReLU Convolution, ReLU Convolution, ReLU Left input patch Convolution, ReLU Convolution, ReLU Convolution, ReLU Similarity score

Fast Architecture

Dot product Left input patch Convolution Convolution, ReLU Convolution, ReLU Normalize Similarity score Right input patch Convolution Convolution, ReLU Convolution, ReLU Normalize

• J. Zˇbontar & Y. LeCunn “Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches”, JMLR 2015.

Results - KITTI 2015

Left input image Right input image Ground truth

Fast architecture Error: 2.79 % Accurate architecture Error: 2.36 %

• J. Zˇbontar & Y. LeCunn “Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches”, JMLR 2015.

More Data?

• N. Mayer, D. Cremers, T. Brox, et al. “A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation”, CVPR 2016.

More Data?

• N. Mayer, D. Cremers, T. Brox, et al. “A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation”, CVPR 2016.

FlowNet

• P. Fisher, D. Cremers, T. Brox, et al. “FlowNet: Learning Optical Flow with Convolutional Networks”, ICCV 2015.

FlowNet - Refinement

• P. Fisher, D. Cremers, T. Brox, et al. “FlowNet: Learning Optical Flow with Convolutional Networks”, ICCV 2015.

FlowNet - Results

Images Ground truth EpicFlow FlowNetS FlowNetC

• P. Fisher, D. Cremers, T. Brox, et al. “FlowNet: Learning Optical Flow with Convolutional Networks”, ICCV 2015.