Comparative study of two motion detection algorithms

1. Mrs. K. V. Joshi
Asst. Professor (E&Tc Dept.)
SIT College of Engg
Yadrav-Ichalkaranji

2. Contents
 Image Processing
 What is an image
 Digital Image Representation
 Various Color Models
 Object Detection
 Object Tracking
 Challenges in Object Detection and Tracking
 Object Tracking Approaches
 Applications of Object Detection and Tracking
 Steps involved in Object Detection and Tracking

3.  CAMShift
 Particle Filter
 Implementation and Results
 Discussion
 References

4. Image Processing
 In imaging science, image processing is processing of
images using mathematical operations by using any form
of signal processing for which the input is an image, a
series of images, or a video, such as a photograph or video
frame; the output of image processing may be either an
image or a set of characteristics or parameters related to
the image.

5. What is an Image?
 An image may be defined as a two-dimensional function ,
f(x,y), where x and y are spatial (plane) coordinates, and
the amplitude of f at any pair of coordinates(x, y) is called
the intensity or gray level of the image at that point.
 When x, y, and the intensity values of f are all finite,
discrete quantities, we call the image a digital image. The
field of digital image processing refers to processing digital
images by means of a digital computer.
 A digital image is composed of a finite number of
elements, each of which has a particular location and
value. These elements are called picture elements, image
elements, pels, and pixels. Pixel is the term used most
widely to denote the elements of a digital image.

6. Digital Image Representation
 An image can be defined as a 2D signal that varies over
spatial co-ordinates x and y and can be written
mathematically as f(x,y)
 Binary Image Equivalent Image contents in Binary Form
1 1 1 1 1
0 1 1 1 0
0 1 1 1 0
0 1 1 1 0
0 1 1 1 0

7. Image Color Models
 RGB
 CMY
 HSV
 HSI

8. RGB Color Model
 In the RGB model, an image
consists of three independent
image planes, one in each of the
primary colors: red, green and
blue. (The standard wavelengths
for the three primaries are as
shown in figure ). Specifying a
particular color is by specifying the
amount of each of the primary
components present.
Figure shows the geometry of the
RGB color model for specifying
colors using a Cartesian coordinate
system. The greyscale spectrum,
i.e. those colors made from equal
amounts of each primary, lies on
the line joining the black and
white

9. CMY Color Model
 subtractive color model appropriate to absorption of
colors, for example due to pigments in paints. Whereas
the RGB model asks what is added to black to get a
particular color, the CMY model asks what is subtracted
from white. In this case, the primaries are cyan, magenta
and yellow, with red, green and blue as secondary colors
 When a surface coated with cyan pigment is illuminated
by white light, no red light is reflected, and similarly for
magenta and green, and yellow and blue. The
relationship between the RGB and CMY models is given
by:

10. HSI Color Model
 As mentioned above,
color may be specified
by the three quantities
hue, saturation and
intensity. This is the
HSI model, and the
entire space of colors
that may be specified
in this way is shown in
figure.

11. YIQ Color Model
 The YIQ (luminance-in-phase-quadrature) model
is a recording of RGB for color television, and is a
very important model for color image processing.
 The conversion from RGB to YIQ is given by:

𝑌
𝐼
𝑄
=
0.299 0.587 0.114
0.596 −0.275 −0.321
0.212 −0.523 0.311
𝑅
𝐺
𝐵

12. HSV Color Model
 HSV stands for hue, saturation,
and value
 HSV is the most common cylindrical
co-ordinate representation of points in
an RGB color model. This
representation rearranges the geometry
of RGB in an attempt to be more
intuitive and perceptually relevant than
the Cartesian (cube) representation.
Developed in the 1970s for computer
graphics applications, HSV is used
today in color pickers, in image
editing software, and less commonly in
image analysis and computer vision.

13. Video Processing
 A video can be considered as a collection of images
indexed by time.
 Video processing is an extension of image processing.
 Digital video comprises a series of orthogonal
bitmap digital images displayed in rapid succession at
a constant rate. In the context of video these images
are called frames. We measure the rate at which
frames are displayed in frames per second (FPS).

14. Object detection
 Object detection is a method of following an object
through successive image frames to determine its
relative movement with respect to other frames.
 Object detection in videos involves verifying the
presence of an object in image sequences and possibly
locating it precisely for recognition

15. Object Tracking
 Object tracking is a process of segmenting a region of
interest from a video and keep tracking of the motion,
position and also the occlusion.
 The tracking is performed by monitoring objects’
temporal and spatial changes during the video
sequence, also its presence, position, size, shape, etc.

16. Challenges in Object Detection and
Tracking
The detection and tracking of objects in video can be complex
due to:
1. Loss of information caused by projection of the 3D world on
a 2D image,
2. Noise in images,
3. The complex object motion,
4. Non-rigid or the articulated nature of the objects,
5. Partial and full object occlusions,
6. Scene illumination changes
7. Complex object shapes,
8. The real time processing requirements.

17. Object Tracking Approaches
Based on the above information there are three basic
approaches for object tracking,
 Point tracking approach.
 Kernel tracking approach.
 Silhouette tracking approach.

18.  Video Surveillance
 Robot Vision
 Traffic Monitoring
 Video Inpainting (Video Inpainting Refers To A Field Of Computer
Vision That Aims To Remove Objects Or Restore Missing Or Tainted
Regions Present In A Video Sequence By Utilizing Spatial And
Temporal Information From Neighboring Scenes.)
 Animation
 Surveillance/Monitoring Applications Surveillance/Monitoring
Applications
 Security Cameras
 Traffic Monitoring
 Face Detection
 People Counting
 Vehicle Detection
Applications of Object Detection
and Tracking

19.  Online Images
 Security
 Manufacturing Industry
 Control Applications
 Object Avoidance Object Avoidance
 Automatic Guidance
 Head Tracking for Video Head Tracking for Video
Conferencing
 Many intelligent video analysis systems are intelligent
video analysis systems are based on motion detection and
tracking

20. 
Input Video
Extract frames from
input video
Object Detection
Object Representation
Object Tracking
Moving Object
Steps involved in Object Detection
and Tracking:

21. CAMShift
 The Camshift algorithm is improved by Meanshift
algorithm, and it is called Continuously Adaptive
Mean Shift algorithm.
 The basic idea of Camshift is to use the Meanshift to
all frames of the video image, and take the result of
previous frame as initial value to the next frame, so
iteration.

22. Steps Of Camshift Algorithm
The Camshift algorithm can be summarized in the following
steps:
(1) Initialize the search window.
(2) Calculate the color probability distribution of the search
window (back projection).
(3) Run Meanshift algorithm, obtain the new size and
position of the search window.
(4) Re-initialize the size and position of the search window
in the next frame of video image by using the value that
calculated in step (3), and jump to step (2) to proceed.

23. Particle Filter
 Particle filter is a filtering method based on Monte
Carlo and recursive Bayesian estimation.
 The particle filters, also known as condensation filter
and they are suboptimal filters.
 The core idea is that density distribution is present
using random sampling particles. There is no
restriction to the state vector to deal with nonlinear
and non-Gaussian problem, and it is the most general
Bayesian approach.

24. IMPLEMENTATION
AND RESULTS FOR PARTICLE
FILTER:
 1 For Clean Video
 Elapsed Time: 9.026 sec

25. For Noisy Video
 The three types of noise such as salt & pepper,
Gaussian and Speckle noise was inserted into the
original video and the algorithm was tested.
Salt & pepper noise

26. Gaussian noise

27. Speckle noise

28. Implementation of CAMShift
Algorithm
For Clean Video

29. For Noisy Video
 The three types of noise such as salt & pepper,
Gaussian and Speckle noise was inserted into the
original video and the algorithm was tested

30. Salt and Pepper
Elapsed Time: 21.224 sec

31. Gaussian Noise
Elapsed Time: 14.387 sec

32. Speckle Noise
Elapsed Time: 15.207 sec

33. Intensity Consideration for Both
Algorithms
 Table 1- Nomenclature of videos according to intensity
variations
Video Change in intensity
A1, B1 5%
A2, B2 10%
A3, B3 15%
A4, B4 20%
A5, B5 25%

34. Video Algorithm Track Loss Rate (%) Elapsed Time(seconds)
Original
Particle Filter 29.13 5.25
CAMShift 12 55.70
A1
Particle Filter 32.13 5.05
CAMShift 15 53.70
A2
Particle Filter 35.15 4.82
CAMShift 16.03 50.35
A3
Particle Filter 37.56 4.30
CAMShift 18.05 48.78
A4
Particle Filter 39.54 4.28
CAMShift 20.16 46.07
A5
Particle Filter 40.13 4.10
CAMShift 25.8 43.65
Original
Particle Filter 29.13 5.25
CAMShift 12 55.70
B1
Particle Filter 40.67 17.45
CAMShift 21.26 21.75
B2
Particle Filter 45.65 17.25
CAMShift 22.03 21.08
B3
Particle Filter 49.53 16.23
CAMShift 25.05 20.32
B4
Particle Filter 51.25 15.10
CAMShift 26.16 20.15
B5
Particle Filter 55.14 14.20
CAMShift 29.8 19.42
Table2-Performance Evaluation of Two Algorithms based on
track loss rate and elapsed time

35. Discussion
 As noise is introduced in video, elapsed time increases
for Particle Filter algorithm, but by using CAMShift
algorithm the elapsed time decreases.
 For both algorithms Object detection and tracking is
better in case of Salt and Pepper noise than that of
Gaussian and Speckle Noise.
 For both the videos, as the intensity of original video is
changed, elapsed time decreases and track loss rate
increases.

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