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Video Image and Motion Information

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3-D Computer Vision

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Abstract

Video represents a special kind of sequence image, which describes the radiation intensity of the scene obtained by three separate sensors when a 3-D scene is projected onto a 2-D image plane over time. Digital video can be acquired by means of a digital camera using a CCD sensor or the like. From the perspective of learning image technology, video can be seen as an extension of (still) images. In view of the characteristics of videos containing motion information, the original image processing technology also needs to be extended accordingly. This chapter will introduce the basic content of the video, including the representation, modeling, display and format of the video, and the color model in the color TV system. This chapter will discuss the classification of the motion information in the video compared to the still image, as well as the characteristics and representation methods of foreground motion and background motion. This chapter will discuss the motion detection methods using image difference, the principle of motion detection in the frequency domain, and the detection of the direction of motion. This chapter will discuss the filtering methods that combine the characteristics of the video and consider the motion information.

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References

  1. Tekalp A M. Digital Video Processing. UK London: Prentice Hall. 1995.

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Yu-Jin Zhang

Authors
  1. Yu-Jin Zhang
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Self-Test Questions

Self-Test Questions

The following questions include both single-choice questions and multiple-choice questions, so each option must be judged.

  1. 4.1

    Video Basic

    1. 4.1.1

      Video has many different representation forms; among the three formats introduced, (·).

      1. (a)

        The data volume of the component video format is the smallest.

      2. (b)

        The data volume of the S-video format is the smallest.

      3. (c)

        The quality of S-video format is the worst.

      4. (d)

        The quality of the composite video format is the worst.

    [Hint] The three formats are sorted by data volume and quality, respectively.

    1. 4.1.2

      In the practical YCBCR color coordinate system, (·).

      1. (a)

        The maximum value of CB can only be obtained at one point.

      2. (b)

        The minimum value of CR can only be obtained at one point.

      3. (c)

        The value range of Y is smaller than the value ranges of CB and CR.

      4. (d)

        The value range of Y is larger than the value ranges of CB and CR.

    [Hint] Refer to the representation of RGB color space.

    1. 4.1.3

      The video bit rate (b/s) of NTSC color TV system is (·).

      1. (a)

        249 M

      2. (b)

        373 M

      3. (c)

        498 M

      4. (d)

        746 M

    [Hint] Calculate according to Eq. (4.4).

  2. 4.2

    Motion Classification and Representation

    1. 4.2.1

      Between the foreground motion and background motion, (·).

      1. (a)

        The foreground motion is more complicated.

      2. (b)

        Background motion is more difficult to detect than foreground motion.

      3. (c)

        The foreground motion is related to the motion of the camera.

      4. (d)

        Background motion generally has the characteristics of strong integrity.

    [Hint] Foreground motion is also called local motion; background motion is also called global motion or camera motion.

    1. 4.2.2

      By observing the image obtained through superimposing the calculated motion vector on the original image, (·).

      1. (a)

        The image can be divided into blocks.

      2. (b)

        The spatial position of the motion can be determined.

      3. (c)

        It can understand the magnitude and direction of the motion.

      4. (d)

        It can distinguish background motion and foreground motion.

    [Hint] The vector has magnitude and direction, and the vector superimposed on the original image describes the motion speed of the image block.

    1. 4.2.3

      When the figure in Fig. 4.16 rotates clockwise around the center, the figure superimposed with the optical flow field vector is the closest to (·).

      1. (a)

        Fig. 4.17a

      2. (b)

        Fig. 4.17b

      3. (c)

        Fig. 4.17c

      4. (d)

        Fig. 4.17d

    [Hint] When drawing the line segment representing the vector, pay attention to the starting point and length, where the length is proportional to the linear velocity of motion.

  3. 4.3

    Motion Information Detection

    1. 4.3.1

      The six motion types of the camera can be combined to form three types of operations, among which the translation operations include (·).

      1. (a)

        Scanning, tilting, zooming

      2. (b)

        Tilting, zooming, tracking

      3. (c)

        Zooming, tracking, lifting

      4. (d)

        Tracking, lifting, dollying

    [Hint] Analyze the camera motion conditions represented by various motion types in detail.

    1. 4.3.2

      Suppose the motion vector of a point in the image is [3, 5], then the values of the coefficients in its six-parameter motion model are (·).

      1. (a)

        k0 = 0, k1 = 0, k2 = 3, k3 = 0, k4 = 0, k5 = 5

      2. (b)

        k0 = 0, k1 = 3, k2 = 0, k3 = 0, k4 = 0, k5 = 5

      3. (c)

        k0 = 0, k1 = 0, k2 = 3, k3 = 0, k4 = 5, k5 = 0

      4. (d)

        k0 = 0, k1 = 3, k2 = 0, k3 = 0, k4 = 5, k5 = 0

    [Hint] Substitute into Eq. (4.12) to calculate.

    1. 4.3.3

      For detecting the change of object scale in the frequency domain, (·).

      1. (a)

        It needs to calculate the Fourier transform phase angle of the object image.

      2. (b)

        It needs to calculate the Fourier transform power spectrum of the object image.

      3. (c)

        If the scale change value is greater than 1, it indicates that the object size has increased.

      4. (d)

        If the scale change value is smaller than 1, it indicates that the object size has reduced.

    [Hint] Analyze the meaning of each parameter in Eq. (4.32).

  4. 4.4

    Motion-Based Filtering

    1. 4.4.1

      Motion adaptive filtering (·).

      1. (a)

        Is a filtering method based on motion detection

      2. (b)

        Is a filtering method based on motion compensation

      3. (c)

        Uses the motion information between adjacent frames to determine the filtering direction

      4. (d)

        Detects the changes in noise intensity along the time axis

    [Hint] Analyze the characteristics of motion adaptive filtering in detail.

    1. 4.4.2

      Which of the following statement(s) is/are correct? (·).

      1. (a)

        It is difficult to design an infinite impulse response filter.

      2. (b)

        The infinite impulse response filter updates its response iteratively.

      3. (c)

        The finite impulse response filter uses feedback to limit the output signal length.

      4. (d)

        The output signal of the finite impulse response filter is finite when the input signal is infinite.

    [Hint] Analyze according to Eqs. (4.38) and (4.39).

    1. 4.4.3

      In the filtering based on motion compensation, (·).

      1. (a)

        Suppose the gray scale of moving pixels is constant.

      2. (b)

        Consider that all points in the scene are projected onto the XY plane.

      3. (c)

        Suppose that the trajectory of the point is a straight line along the time axis.

      4. (d)

        Need to apply the motion compensation filter to the motion trajectory.

    [Hint] Analyze the characteristics of motion compensation filtering.

Fig. 4.16
A circle divided into 4 equal parts with two dark and two light shades.

Original figure

Full size image
Fig. 4.17
4 circles, each divided into 4 equal parts with two dark and two light shades and lines marked on each circle in different lengths.

Figures superimposed with optical flow vectors

Full size image

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Zhang, YJ. (2023). Video Image and Motion Information. In: 3-D Computer Vision. Springer, Singapore. https://doi.org/10.1007/978-981-19-7580-6_4

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  • DOI: https://doi.org/10.1007/978-981-19-7580-6_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-7579-0

  • Online ISBN: 978-981-19-7580-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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