ECE 634 – Digital Video SystemsSpring 2019

Fengqing Maggie ZhuAssistant Professor of ECE

MSEE 334zhu0@ecn.purdue.edu

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Motion Models

Outline

• Camera model and camera motion• Object motion• Models to represent motion

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Why Study Motion?

• Motion creates spatio-temporal information• Understand 3D motion• Better object recognition, identification, segmentation• Sometimes the information IS the motion (ex: action

recognition)• Improve video quality through motion stabilization

• Objects themselves don’t change much during motion

• Remove temporal redundancies for compression• Motion-compensated temporal filtering (along motion

trajectories) to remove noise• Motion-compensated frame interpolation

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Example Videos

• Akiyo: object motion• Bus: object + camera motion• City: camera motion

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https://www.biomotionlab.ca/Demos/BMLwalker.html5

Reading Material

• A. M. Tekalp, Digital video processing, Prentice Hall, 2015

• Chapter 4.1,4.2

• Y. Wang, J. Ostermann, and Y.-Q. Zhang, Video Processing and Communications, Prentice Hall, 2002

• Chapter 5.1, 5.3.2, 5.5

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Motion Detection

• Is an image region moving or stationary?• We will postpone this discussion until later

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Motion Estimation

• Need accurate models for the motion• Understand the camera and its motion• Understand objects and their motion

• All we can see from the camera is the 2D projection of the 3D world

• This mapping is not unique! Many 3D-worlds can produce the same 2D image

• All our interpretations of the world must take place through this projection

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Motion Estimation Applications

• Motion for interpreting 3D world requires an inverse mapping

• Motion for compression need not approximate physical motion; it is solely to reduce data rate

• Processing with motion is best if the true motion of image points can be obtained

Different applications/purposes of motion require different approaches for motion estimation

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To Estimate 2D Motion We Need

• A motion model• An estimation criterion• A search strategy

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Summary of Motion Models

• What causes 2D motion?• Camera motion• Object motion projected to 2D

• Camera model: 3D à 2D projection• Perspective projection vs. orthographic projection

• Models corresponding to typical camera motion and object motion

• Piece-wise projective mapping is a good model for projected rigid object motion

• Can be approximated by affine or bilinear functions• Affine functions can also characterize some global camera motions

• Ways to represent motion• Pixel-based, block-based, region-based, global, etc.

2D Motion

2D (or apparent) motion that is created by moving objects depends on 3 things:1. An image formation (or camera) model

• Perspective, orthographic, ..2. Motion model of a 3D object (rigid body with 3D

translation and rotation, 3D affine motion)3. Surface model of 3D object (planar, parabolic..)

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Camera Models

• Orient the camera in 3D space• Basic camera model (pinhole)• Simpler orthographic model• More complex camera models exist

• Example: CAHV – characterizes practical camera system by its extrinsic and intrinsic parameters

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Pinhole Camera

Cameracenter

Imageplane

2-Dimage

3-Dpoint

The image of an object is reversed from its 3-D position.

Perspective: The object appears smaller when it is farther away.

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Pinhole Camera Model:Perspective Projection

ZyxZYFy

ZXFx

ZY

Fy

ZX

Fx

torelatedinversely are ,

,, ==Þ==

All points in this ray will have the same image

Z is depth;Distance fromcamera centerto object

Attributes of Perspective Projection

• Straight lines in 3D space are projected into straight lines in 2D space

• Parallel lines in 3D may not be parallel in 2D• Vanishing points

• A nonlinear model, due to division by depth Z

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Vanishing Points

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Simpler Model:Orthographic Projection

object. theof distance the tocomparedsmall isobject in theation withdepth vari theas long as used beCan

,)(far very isobject When the

YyXxZ

==¥®

CAHV camera model

• (extrinsic) Vector C: location of the pinhole• (extrinsic) Vector A: Camera axis (normal to the

image plane)• (intrinsic) Vector H:

• Horizontal axis of image plane• H-coordinate of optical center of image plane• Horizontal focal length (in pixels)

• (intrinsic) Vector V: same vertically

A number of other camera models available, all more sophisticated than pinhole model19

Translational Camera Motions in 3D

Track (x), Boom (y), Dolly (z)

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Rotational Camera Motions in 3D

Tilt (x), Pan (y), Roll (z)

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2D Motion

2D (or apparent) motion that is created by moving objects depends on 3 things:1. An image formation (or camera) model

• Perspective, orthographic, ..2. Motion model of a 3D object (rigid body with 3D

translation and rotation, 3D affine motion)3. Surface model of 3D object (planar, parabolic..)

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Motion models for 3D objects

• 3D motion • Projection of 3-D motion on 2-D image plane• 2D motion caused by rigid object motion

• Projective mapping• Approximations to the 2D motion field

• Affine model• Bilinear model

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Rigid Object 3-D Motion

• Translation vector T, defines how object translates in x,y,z; T=[Tx,Ty,Tz]’

• Rotation matrix R• Defines how object rotates, first in X, then in Y, then in Z• Defined by rotation angles:

• 6 parameters, valid for all points on object

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zyx qqq ,,

Rotation Matrix

[R] is orthonormal, i.e. it satisfies

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Linearization Approximation

• Assume rotation angles are small so that

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Rigid Object 3-D Motion

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zyxzyx TTT ,,:;,,:][;)]([':centerobject theon wrp. translatiandRotation

TRCTCXRX qqq++-=

Rigid vs Flexible Object

• An object is rigid, if all its points move with the same 3D motion parameters

• Two ways to describe flexible object• Decompose into multiple, but connected rigid sub-

objects• Global motion plus local motion in sub-objects• Ex. Human body consists of many parts each undergo a

rigid motion

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3-D Motion à 2-D Motion

2-D MV

3-D MVRemember: Many 3D MV’smap to the same 2D MV.So the inverse problem isill-defined.

Sample 2-D Motion Field

A “needle” plot helps to visualize motion, bydescribing where to go in first frame to get the “matching” pixel values for the second frame

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2D Motion Models

• How is the 2D motion represented in 2D• Each pixel can move on its own, but it’s often more

effective to describe how a region of pixels move –because remember, 2D motion is caused either by camera motion (which moves all pixels) or by objectmotion (which moves all the pixels on an object)

• So next, we look at how camera motion appears in 2D

• Take a point (X,Y,Z) in 3D, map it into its new coordinates (X’,Y’,Z’) based on camera motion, the apply perspective mapping to find relationship between (x,y) and (x’,y’)

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2-D Motion Corresponding to Camera Motion

Camera zoom Camera rotation around Z-axis (roll)

Also Pan and tilt (with small angle approximations)

Camera Track And BoomTranslation: Tx , Ty

3D position of any point:

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The imaged position: When Z is large-ish:

Camera Pan and TiltRotation angles: 𝜃y ,𝜃 x

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When rotation angle are small:

[Rx] and [Ry] as defined earlier

[R]

Camera ZoomF and F’ are focal length before and after

is the zoom factor

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The 2D motion field is

Camera RollRotation around the Z-axis, no change in depth

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The 2D motion field is

Four Parameter Model

• Consider a camera that goes through translation, pan, tilt, zoom, and rotation in sequence

• With small angle approximation:

• A special case of the affine mapping, which generally has six parameters.

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2-D Motion Corresponding to Rigid Object Motion• General case:

• Projective mapping:

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FTZFryrxrFTZFryrxr

Fy

FTZFryrxrFTZFryrxrFx

TTT

ZYX

rrrrrrrrr

ZYX

z

y

z

x

z

y

x

+++

+++=

++++++

=

¾¾¾¾¾¾ ®¾

úúú

û

ù

êêê

ë

é+úúú

û

ù

êêê

ë

é

úúú

û

ù

êêê

ë

é=

úúú

û

ù

êêê

ë

é

)()(

'

)()('

'''

987

654

987

321

Projection ePerspectiv

987

654

321

ycxcybxbby

ycxcyaxaax

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210

21

210

1',

1'

:c)bYaX(Zplanar is surfaceobject When the

++++

=++++

=

++=

(8 parameters)

More 2D motion models

• Projective mapping (8 parameters)• (3D rigid motion of planar surface using perspective

projection)• Affine

• (3D rigid motion of planar surface using orthographic projection)

• Bilinear• Translational

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Projective Mapping

Two features of projective mapping:• Chirping: increasing perceived spatial frequency for far

away objects• Converging (Keystone): parallel lines converge in distance

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Affine Model

• Polynomial approximation to projective mapping• 6 parameters

• Maps triangles to triangles: defined by motion vectors of the three corners

úû

ùêë

é++++

=úû

ùêë

éybxbbyaxaa

yxdyxd

y

x

210

210

),(),(

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Affine Model

Affine model:• No chirping, no converging of parallel lines

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Bilinear Model

• Another approximation to projective mapping• 8 parameters

• Maps a square into a quadrilateral • CANNOT map between 2 arbitrary quadrilaterals

úû

ùêë

é++++++

=úû

ùêë

éxybybxbbxyayaxaa

yxdyxd

y

x

3210

3210

),(),(

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Bilinear Model

Bilinear model:• No chirping, but convergence of parallel lines

Other Polynomial Models

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Motion Field Corresponding toDifferent 2-D Motion Models

Translation

Bilinear Projective

Affine

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Region of Support for Motion Representation

Global:Entire motion field is represented by a few global parameters

Pixel-based:One MV at each pixel, with some smoothness constraint between adjacent MVs.

Region-based:Entire frame is divided into regions, each region corresponding to an object or sub-object with consistent motion, represented by a few parameters.

Block-based:Entire frame is divided into blocks, and motion in each block is characterized by a few parameters.

Practice Problem

Show that Eq. (1) can be simplified into the projective mapping Eq. (2) when the imaged object has a planar surface, that is

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(1)

(2)

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