objecg detection and tracking

2. 1. Introduction to object tracking
2. Object representation
3. Feature selection for tracking
4. Algorithm for object tracking
5. Introduction to object detection
6. Moving object detection algorithms
7. Advantages and disadvantages of moving object
detection
8. Conclusion

3.  A method of following an object through successive image
frames
 To determine its relative movement with respect to other
objects.

4. • Visual input is usually achieved through digitized images
obtained from a camera connected to a digital computer.
• This camera can be either stationary or moving depending on the
application.
• Beyond image acquisition, the computer performs the necessary
tracking and any higher level tasks using tracking result.

5.  In a tracking scenario, an object can be defined as anything that is of
interest for further analysis.
 Objects can be represented by their shapes.
Object shape representations commonly employed for tracking are:
•Points: The object is represented by a point, that is, centroid or
set of points. Point representation is suitable for tracking objects
that occupy small regions in an image.
•Primitive geometric shapes: Object shape is represented by a
rectangle, ellipse etc. these are suitable for representing simple
rigid objects and non rigid objects.

6. •Object silhouette and contour: contour representation defines the
boundary of an object. The region inside the contour is called the silhouette

8. The common visual features are as follows:
•Color: The apparent color of an object is influenced by two
spectral power distribution of illuminant and surface
reflectance properties.
•Edges: Object boundaries generate strong changes in the
intensities. Edge detection is used to identify these changes

9. •Optical Flow: Optical flow is a dense field of displacement vectors which
defines the translation of each pixel in a region. It is computed using the
computed using the brightness constraints.
•Texture: Texture is a measure of the intensity variation of the surface which
quantifies properties such as smoothness and regularity.

10. Background Subtraction in Videos using Bayesian Learning

11. Object detection from video sequence is the process of
detecting the moving objects in frame sequence using digital
image processing techniques.
Challenges of moving object detection:
 Loss of information caused by the 3D world on a 2D image
 Noise in images
 Complex object motion
 Non-rigid or articulated nature of objects
 Partial or full object occlusions
 Complex object shapes
 Scene illumination changes

12. FD BS FD & BS
BUT CC
1.An improved
moving object
detection
algorithm
2.A moving
object
detection
algorithm for
smart cameras
3.An automatic
object
detection
algorithm for
video
surveillance
application
1.Real time
moving
object
detection
for video
monitoring
system
1.A moving
object
detection
algorithm
based on
improved
background
subtraction
1.A moving
object
detection
algorithm on
color
information
1.Detect
& track
moving
object
using
partitio-
ning &
normal-
ized
cross
correlat-
ion

13. • In this method a background image without any moving
objects of interest is taken as reference image.
• Pixel value for each co-ordinate (x, y) for each color channel
of the background image is subtracted from corresponding
pixel value of input image.
• If resulting value is greater than a particular threshold value,
then that is foreground pixel otherwise background.

14. a)Frame(k-1) taken at time t b)Frame(k) taken at time t+1
c)Edge map(EDGE k-1) of Frame(k-1) d)Edge map(EDGE k) of Frame(k)

15. e)Edge difference image D( x, y)
highlighting the difference of
edge maps of Frame(k-1) &
Frame(k)
f)Obtained motion areas are then
mapped to the original image &
appropriate edge pixels(white
pixels) are highlighted
From the above result we can see that the frame differencing
based on the edge detection is a simple method for detecting
for moving objects and gives better results

16. Flow diagram

17. image Signed difference Calculate motion
strength
Detect & Link motion blocks result

18. Steps:
 Moving object detection phase
Moving object extraction phase
Moving object recognition phase

19. 1st Image reference
image
2nd Image input
image
Difference image 3rd image
output
image
Difference
deterministic rule,
Thresholding &
background
compensation

20. •Horizontal Scanning
•Vertical Scanning
Comparison
Deterministic
rule
Threshold
eliminate
phantom
object &
frame
Implementation steps of extraction:

21. The moving object regions are detected by subtracting the
current image pixel-by-pixel from a reference background
image.
The pixel where the difference is above a threshold are
classified as foreground otherwise background.
Morphological post processing operations are performed to
reduce the effects of noise and enhance the detected object.

22. Flowchart of background updating

23. Experiments:
Comparison of background images
Fig 1 Fig 2
Fig1 shows the shadow on right-top corner of the image.
Fig2 shows the background image by using self-adaptive updating of
background image

24. Background image of 35th frame Background image of 105th frame
Background image of 175th frame

25. Moving vehicle of 35th frame Moving vehicle of 105th frame
Moving vehicle of 175th frame

26. The combination of background subtraction and frame
differencing can improve the detection speed and overcome the
lack of sensitivity of light changes.

27. Original images Result obtained after using Gaussian
mixed model

28. masked original image
Foreground image

29. The background updating of the selected pixels are replaced by
the average of the current and background pixels.

30. shows the origin image,
and the color of
pedestrian’s pants is
adjacent to the floor color.
shows the result of origin
MOG detection, and legs
which are overlap with
floor can’t be detected
completely.
shows the detected
result of improved
MOG, and legs which
were overlap with floor
can be detected
completely.

31. shows the color image
segmentation result with
the edged image.
show the final detected
result of joint color image
segmentation and
background model.
background model

32. Partitioning of two
consecutive frames
Moving object detection
using Cross Correlation
Identify moving object’s
location and perform tracking
Basic Steps:

33. The tracking sequence of a walking person. This walking person is pointed by a red
star.
Tracking sequence of multiple objects by simple difference method

34. Tracking sequence of multiple objects by PNCC method

36. Object tracking means tracing the progress of objects as they
move about in visual scene.
Object tracking, thus, involves processing spatial as well as
temporal changes.
Certain features of those objects have to be selected for
tracking.
These features need to be matched over different frames.
Significant progress has been made in object tracking.
Taxonomy of moving object detection is been proposed.
Performance of various object detection is also compared.
So, atlast it is noted that algorithm based on frame difference
and edge detection has detection accuracy and high detection