The document discusses motion representation in computer vision, focusing on concepts like motion fields, optical flow, motion parallax, and feature-based techniques for motion estimation. It highlights the importance of tracking object movements in 3D environments via 2D representations, along with different estimation methods such as pel-recursive algorithms and block-matching algorithms. It also addresses challenges in motion analysis, like occlusion and the complexity of human motion, especially relevant for applications in computer animation and video processing.

1. Computer Vision
Chap.6 : Motion Representation
SUB CODE: 3171614
SEMESTER: 7TH IT
PREPARED BY:
PROF. KHUSHALI B. KATHIRIYA

2. Outline
• The motion field of rigid objects
• Motion parallax
• Optical flow
• The image brightness constancy equation
• Affine flow
• Differential techniques
• Feature-based techniques
• Regularization and robust estimation
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3. Motion Filed and Optical Flow
PREPARED BY:
PROF. KHUSHALI B. KATHIRIYA

4. What is Motion Representation?
• Motion analysis was motivated by the need for
tracking an object and advancement in image
processing hardware.
• Analyzing human motion is a challenging task with
a wide variety of applications in computer vision
and in graphics. One such application, of particular
importance in computer animation, is the
retargeting of motion from one performer to
another. While humans move in three dimensions,
the vast majority of human motions are captured
using video, requiring 2D-to-3D pose and camera
recovery, before existing retargeting approaches
may be applied.
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5. The Motion field
• In computer vision the motion field is an ideal representation of 3D motion as it is projected onto
a camera image. Given a simplified camera model, each point {displaystyle (y_{1},y_{2})}in the
image is the projection of some point in the 3D scene but the position of the projection of a fixed
point in space can vary with time.
• The motion field can formally be defined as the time derivative of the image position of all image
points given that they correspond to fixed 3D points. This means that the motion field can be
represented as a function which maps image coordinates to a 2-dimensional vector. The motion
field is an ideal description of the projected 3D motion in the sense that it can be formally defined
but in practice it is normally only possible to determine an approximation of the motion field from
the image data.
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6. The Motion Field
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7. The Motion Field
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8. The Motion Field
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9. The Motion Field
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10. Optical Flow
• Motion of brightness patterns in the image
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11. When Optical Flow ≠ Motion Filed?
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12. When Optical Flow ≠ Motion Filed?
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13. Motion Illusion
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An affine (or first-order) optic flow model has 6 parameters, describing image translation, dilation,
rotation and shear. The class affine_flow provides methods to estimates these parameters for two
frames of an image sequence. (we have seen in 1st chap.)

15. Motion Parallax
PREPARED BY:
PROF. KHUSHALI B. KATHIRIYA

16. Motion Parallax
• Motion parallax refers to the fact that objects moving at a constant speed across the
frame will appear to move a greater amount if they are closer to an observer (or
camera) than they would if they were at a greater distance.
• This phenomenon is true whether it is the object itself that is moving or the
observer/camera that is moving relative to the object. The reason for this effect has to
do with the amount of distance the object moves as compared with the percentage of
the camera's field of view that it moves across.
• Ref. video: https://youtu.be/ANQtiQqfEtA
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17. Feature-based Techniques
PREPARED BY:
PROF. KHUSHALI B. KATHIRIYA

18. Feature-based Techniques
• The method of finding image displacements which is easiest to understand is the feature-
based approach. This finds features (for example, image edges, corners, and other structures
well localized in two dimensions) and tracks these as they move from frame to frame. This
involves two stages. Firstly, the features are found in two or more consecutive images.
• The act of feature extraction, if done well, will both reduce the amount of information to be
processed (and so reduce the workload), and also go some way towards obtaining a higher
level of understanding of the scene, by its very nature of eliminating the unimportant parts.
Secondly, these features are matched between the frames. In the simplest and commonest
case, two frames are used and two sets of features are matched to give a single set of
motion vectors.
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19. Feature-based Techniques
• Successive video frames may contain the same objects (still or moving). Motion estimation
examines the movement of objects in an image sequence to try to obtain vectors
representing the estimated motion. Motion compensation uses the knowledge of object
motion so obtained to achieve data compression. In interframe coding, motion estimation
and compensation have become powerful techniques to eliminate the temporal redundancy
due to high correlation between consecutive frames.
• In real video scenes, motion can be a complex combination of translation and rotation. Such
motion is difficult to estimate and may require large amounts of processing. However,
translational motion is easily estimated and has been used successfully for motion
compensated coding.
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20. Feature-based Techniques
• Most of the motion estimation algorithms make the following assumptions:
1. Objects move in translation in a plane that is parallel to the camera plane, i.e., the effects of
camera zoom, and object rotations are not considered.
2. Illumination is spatially and temporally uniform.
3. Occlusion of one object by another, and uncovered background are neglected.
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21. Feature-based Techniques
• There are two mainstream techniques of motion estimation:
1. pel-recursive algorithm (PRA)
2. block-matching algorithm (BMA).
• RAs are iterative refining of motion estimation for individual pels by gradient methods. BMAs
assume that all the pels within a block has the same motion activity. BMAs estimate motion
on the basis of rectangular blocks and produce one motion vector for each block. PRAs
involve more computational complexity and less regularity, so they are difficult to realize in
hardware. In general, BMAs are more suitable for a simple hardware realization because of
their regularity and simplicity.
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23. Feature-based Techniques
• Figure illustrates a process of block-matching algorithm. In a typical BMA, each frame is
divided into blocks, each of which consists of luminance and chrominance blocks. Usually, for
coding efficiency, motion estimation is performed only on the luminance block. Each
luminance block in the present frame is matched against candidate blocks in a search area on
the reference frame. These candidate blocks are just the displaced versions of original block.
• The best (lowest distortion, i.e., most matched) candidate block is found and its
displacement (motion vector) is recorded. In a typical interframe coder, the input frame is
subtracted from the prediction of the reference frame. Consequently the motion vector and
the resulting error can be transmitted instead of the original luminance block; thus
interframe redundancy is removed and data compression is achieved. At receiver end, the
decoder builds the frame difference signal from the received data and adds it to the
reconstructed reference frames. The summation gives an exact replica of the current frame.
The better the prediction the smaller the error signal and hence the transmission bit rate.
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