Image processing

1. A
Project Report
On
Multiple Object Tracking Using Particle Filter
By
D.Srikanth, 13FE1D5804, 1st M. Tech.
Under The Esteemed Guidance Of
Mr. K.Sriraman, Associate Professor.
VIGNAN’S LARA INSTITUTE OF TECHNOLOGY AND SCIENCE
Department Of Computer Science And Engineering

2. Contents
• Introduction
Particle Filter
• Literature Survey
Background Subtraction-Based Multiple Object Tracking Using Particle Filter.
used Background subtraction algorithm
A Particular Object Tracking in anEnvironment of Multiple Moving Objects.
used Region Based Tracking
Tracking Occluded Objects using Kalman Filter.
uses partial and Full occlusion
• Algorithms
Likelihood function
Probability Distribution
• Differences

3. What is Particle….?
• A Particle is a least amount part of an object in
an image.
• An object contains one or more number of
particles in an image.

4. What is filter….?
• Used for to reduce/remove unnecessary
things in an image.
• Such as noise etc..,

5. What is Particle Filter…?
• Mainly used for to detect/track the objects.
• Used by applying different colors to different
objects to remove the unnecessary particles
surrounded by an object.

6. Object Detection

7. • Mainly used in video surveillance system such
as traffic monitoring etc..
• Particle filter use color information for
tracking objects.
• We apply the colors by using RGB values.
• Several algorithms are used in particle filters.
Eg : PDA,JPDA etc..

8. Likelihood Function..
• This fun. is used for to reduce the no. of
particles surrounded on the object.
• Particle that lies on the obj. have some RGB
value. Particle that lies outside of the obj.
have Some other RGB value.(RGB=0)
• Particles which are having more weight will
generate new particles near them and
remaining are moved on to the obj.

9. Likelihood algorithm..
• Beginning of Algorithm
Create particles randomly
For each frame
If |New frame-reference frame I > threshold)
Foreground
End If
Else
Background
End Else
Calculate likelihood
Move particles
Display particles
End of for loop
End of Algorithm

10. A Particular Object Tracking in anEnvironment
of Multiple Moving Objects
• Background image initialization.
• Background subtraction.
• Background image update.

11. Flowchart

12. Background Image Update

13. Similarities b/w paper –I & paper-II
• We use background Subtraction algorithm.
• Use Particle Filter(For Tracking).
• Take Refernce Frames(For Detection).

14. Differences b/w paper –I & paper-II
Paper-I
• We use color
information(RGB values).
• Uses likelihood function.
• PF Gives aggragate when
an occlusion occurs.
Paper-II
• We use Object Location.
• Uses Probability
Distribution.
• PF estimates accurate
results when we are
using object locations.

15. Continued....
• PF gives different values
when there is color
resolution.
• PF gives robust object
tracking framework
under ambiguity
conditions.

16. What is occlusion…?
• It is a set of points that appear in one image
whose corresponding points are not visible in
other image because an opaque obj. is
blocking the view of those points in the
another image.
or
• It is a blockage of an object when we are
tracking another object.