Tracking - CVG @ ETHZ · Computer Vision Tracking Loop search Region actual object position from...

ComputerVision

TrackingTracking

Many thanks to: H. Bischof, B. Leibe, V. Ferrari, K. Graumann, Y. Ukrainitz, D. Wagner, V Lepetit, M. Breitenstein, P. Sabzmeydani, Z. Kalal from whom I borrowed many slides and videos.

ComputerVision

CV

PR

’06]

We all know what tracking is, right?

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ComputerVision Tracking

actual object position

Time t+1Time t

„ FIND IT AGAIN“

ComputerVision What to track?


centerpoint


multiplepoints


(body)parts


region


outline


structure

ComputerVision Approaches

(i) Model-based trackingapplication-specific

human body, faces, space shuttle,…

ComputerVision Approaches

(i) Model-based trackingapplication-specific

human body, faces, space shuttle,…

(ii) Feature trackingmore genericcorner tracking

blob/contour trackingintensity profile tracking

region tracking

ComputerVision

Saliency

Object

Tracking Cues

Model/ Tracking History

Scene

ComputerVision Applications!

• Structure-from-Motion

• Gesture/Action Recognition

• Video editing

• Augmented Reality• Augmented Reality

• Navigation

• ….

ComputerVision Applications: Game Interface

ComputerVision Applications: SfM

• Tracked Points gives correspondences

ComputerVision Applications: SfM

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ComputerVision

Applications: Analysis of Motion Pattern

Single-Agent Level

Multi-Agent Level Scene Level

Detail Level

ComputerVision

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ComputerVision Outline

• Point Tracking• Template Tracking• Region Tracking• Model-based Tracking• Foreground vs. Background• Foreground vs. Background• Tracking-by-Detection

– Object classes– specific object

• Combining Tracking and Detection• Context in Tracking

ComputerVision

x

y

Vision = Inverse Graphics

x

y

z

ComputerVision Motion

ComputerVision

x

y

Vision = Inverse Graphics

x

y

z

Tracking = Inverse Animation

ComputerVision Steps of Tracking

predictpredict correctcorrect

• Recap: Particle filtering– Tracking can be seen as the process of

propagating the posterior distribution of state given measurements across time.

ComputerVision )|,( 111 −−− ttt zppp &

)|,( 1−ttt zppp &

prediction CONDEN

Particle Filter

)|( tt pzpweighing with

)|,( ttt zppp &

update

NSATION

ComputerVision General Tracking Loop

predict to t+1

time t

measure at t+1

update locationupdate model

ComputerVision

Point TrackingPoint Tracking

ComputerVision Estimate Optimal

Transformation

ComputerVision Simple 1D Problem

I0(x)

I (x+h)I1(x+h)

ComputerVision Sum of Squared Differences

I0(x)

I (x+h)

h

I1(x+h)

E(h) = [I0(x) – I1(x+h) ]2

ComputerVision Calculation of Displacement

E(h) [ I0 (x) – I1(x) – hI1’(x) ]2≈

E(h) = [I0(x) – I1(x+h) ]2

-2[I1’(x)(I0(x) – I1(x) ) – hI1’(x)2] h

E

∂∂ ≈

I0(x) – I1(x)h I1’(x)≈

ComputerVision Interpretation

h I0(x)

I (x+h)

I0(x) – I1(x)

I1’(x)I1(x+h)

I0(x) – I1(x)h I1’(x)≈

I1’(x)

ComputerVision Problem A: Local Minima

(a) (b)

ComputerVision Problem A: Local Minima

ComputerVision Problem B: Zero Gradient

I(x) - I0(x)h ≈

I0’(x)

?

ComputerVision Problem B: Aperture problem

No gradient along one direction:

ComputerVision “Solving” the Aperture Problem

• How to get more equations for a pixel?

• Spatial coherence constraint: Pixel’s neighbors have the same movementneighbors have the same movement

I0(x)

I1(x+h)

ComputerVision

, I

I x ∂∂= ,

∂∂=y

II y

t

II t ∂

∂=

Recall: Optical Flow

0 =++ tyx IvIuI

1 equation in 2 unknowns

, =dt

dxu

dt

dyv =

, x

I x ∂= ,

∂=

yI y

tI t ∂

=

ComputerVision Least Squares Problem

Pseudo Inverse

Over determined System of Equations

ComputerVision Eigenvectors of ATA

• Haven’t we seen an equation like this • Haven’t we seen an equation like this before?

• Recall the Harris corner detector!

• “Good Features to Track”

ComputerVision Interpreting the Eigenvalues

λ2

“Corner”λλλλ1 ~ λλλλ2 and large

“Edge” λλλλ2 >> λλλλ1

λ1

“Edge” λλλλ1 >> λλλλ2

“Flat” region

ComputerVision

Samples: Edge / Low Texture / High Texture

ComputerVision Example

ComputerVision

Template TrackingTemplate Tracking

ComputerVision

Lucas-KanadeTemplate Tracker

• From Points to templates

• Estimate „optimal“ warp W

ComputerVision

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& M

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ComputerVision

Tracking by Detection of Local

Image FeaturesImage Features(specific target)

ComputerVision

3D Object Detection

Reference image(s) of the object to detect

Test image

ComputerVision Standard Approach

• Step 1: Keypoint detection– invariant to scale, rotation, or perspective


• Step 2: Patch rectification


• Step 3: Build descriptor vector


• Step 4: Match descriptor vectors

Query

Database

ComputerVision Summary

Search in the Database

Search in the Database

Keypoint Detection

Keypoint Recognition

DatabaseDatabase

Pre-processingMake the actual classification easier

Robust 3D Pose Calculation

(RANSAC)

Robust 3D Pose Calculation

(RANSAC)

Geometric verification

ComputerVision

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AR

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ComputerVision

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ComputerVision

Region TrackingRegion Tracking

ComputerVision Background Modeling

Input Background Model

Moving Foreground

Blobs (Objects)

-

ComputerVision Mean Shift Tracking

• The mean shift tracker tracks a region, with a prescribed (color) distribution

• The similarity between the tracked region and the target region is

an

d M

ee

r, I

CC

V’9

9] region and the target region is

maximized, through evolution towards higher density in a parameter space

• Typically this search only takes a few iterations

[Co

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nd

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CV

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ComputerVision Meanshift Tracking

Region ofinterest (Kernel) Center of

mass

Mean Shiftvector Measurements

ComputerVision Intuitive Description

ComputerVision Intuitive Desciption

ComputerVision Elderly People Monitoring

ComputerVision

Model based Model based Tracking

ComputerVision

Articulated Tracking with Part-Based Model

• part appearance + relative geometry.

ComputerVision Using Models

• Goal– Recover a person’s body articulation

– Detailed parameterization in terms of joint locations or joint angles

• Two basic classes of approaches– Articulated tracking as high-

dimensional inference

– Part-based models

ComputerVision

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ComputerVision

Tracking as On-line Foreground vs.

Background Background Classification

ComputerVision Tracking as Classification

• Learning current object appearance vs. local background.

currentbackgroundbackground

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currentobject

appearance

ComputerVision Tracking as Classification

object

backgroundvs.

ComputerVision Tracking Loop

search Region


from time t to t+1 evaluate classifier on sub-patches

-

+

- -

-

create confidence mapanalyze map and set new

object position update classifier (tracker)

ComputerVision

ComputerVision “Simple tracking”

ComputerVision “Tracking the Invisible”

ComputerVision When does it fail…

ComputerVision

search Region


from time t to t+1

evaluate classifier on sub-patches

-

+

- -

-

create confidence mapanalyze map andset new object

position

update classifier(tracker)

Self-learning!

ComputerVision Drifting

Tracked Patches Confidence

ComputerVision

Tracking by DetectionDetection(object class)

ComputerVision Traditional Tracking

t=1

initialization

t=2position in prev. frame

candidate new positions(e.g., dynamics)

best new position(e.g., max color similarity)

ComputerVision Tracking-by-Detection

…

detect object(s) independently in each frame

associate detections over time into tracks

ComputerVision Multiple Objects

Frame 5Frame 1 Frame 9

ComputerVision

Example: Multiple Object Tracking

ComputerVision How to get the detections?

Persons Background

Supervised Learning

ComputerVision Using the classifier

ComputerVision How to link them?

• Space-Time Analysis:(a) collect detections

Detections

Space Time Volume

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ComputerVision Trajectory Estimation

(a) collect detections(b) trajectory growing and selection

t

x

t

z

Space Time Volume

ComputerVision Trajectory Estimation

(a) collect detections(b) trajectory growing and selection

t

x

t

z

H1H2

Space Time Volume

ComputerVision Result

Input (Object Detections) “Tracking” Result

ComputerVision

ComputerVision More information helps…

• Articulated tracking– “walking” people

• 3D Information

Ground Plane Depth verification

ComputerVision

[Gammeter et al. ECCV’08]

ComputerVision Towards Scene Interpretation

ComputerVision

Combining Tracking and Tracking and

Detection

ComputerVision Refining an object model

• Limit drift

Current Model

Fix (initial) Model

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ComputerVision Recover from Drift


CLICK HERE TO START

ComputerVision Combination: KLT & TbD

• Use a KLT Tracker to explore

• Learn an object detector on the fly.

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ComputerVision

ComputerVision

Contextin Trackingin Tracking

ComputerVision I’m Carl – Track me…

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ComputerVision Tracking Carl

ComputerVision SUPPORTERS…

• … came with different strength.

• … change over time.

ComputerVision SUPPORTERShelp Tracking of…

• … objects which change there appearance very quickly.

• … occluded objects or object outside the image.

• … small and/or low textured objects or even “virtual points”.

ComputerVision ETH-Cup Sequenze

ComputerVision ETH-Cup: Humans

ComputerVision Of the Web Tracker

ComputerVision ETH-Cup: Supporters

ComputerVision Beyond the Image

Supporters

ComputerVision Coupled Motion

Supporters

ComputerVision Changing Supporters

Supporters

ComputerVision

Obviously, there are failure cases……. and magician knows that.

Supporters

ComputerVision

Tracking IssuesTracking Issues

ComputerVision Tracking Requirements

• Strongly depends on the application!

Robust, Accurate, Fast,…

• Constrain the tracking task!

Information about the object, dynamics, environment,…

ComputerVision Tracking Issues

• Initialization

object position

Time t = 0

object position


• Prediction vs. Correction– If the dynamics model is too strong, will end up

ignoring the data

– If the observation model is too strong, tracking is reduced to repeated detectionis reduced to repeated detection

http://www.ethlife.ethz.ch/archive_articles/091008_kalman_per/index


• Obtaining observation…– Generative: “render” the state on top of

the image and compare

– Discriminative: classifier or detector scorescore

• …and dynamics model– specify using domain knowledge

– learn (very difficult)


• Nonlinear dynamics– Sometimes needed

to keep multiple trackers in paralleltrackers in parallel

– E.g., for abrupt direction changes („Persons“)

Wrong prediction

Correctprediction


• Data Association - Multiple Object Tracking– What if we don’t know which

measurements to associate with which tracks?tracks?


• Data Association – Fast Motion


• Data Association – Background / Appearance Change– Cluttered Background

– Changes in shape, orientation, color,…– Changes in shape, orientation, color,…


• Data Association – Occlusions / Self Occlusions


• Model- vs. Model-free-Tracking


• Drift– Errors caused by dynamical model,

observation model, and data association tend to accumulate over time

ComputerVision End.

Tracking - CVG @ ETHZ · Computer Vision Tracking Loop search Region actual object position from...

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