VIGNAN’S LARA INSTITUTE OF TECHNOLOGY AND SCIENCEDepartment Of Computer Science And Engineering

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.

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 functionProbability Distribution

• Differences

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.

What is filter….?

• Used for to reduce/remove unnecessary things in an image.

• Such as noise etc..,

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.

Object Detection

• 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..

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.

Likelihood algorithm..

• Beginning of AlgorithmCreate particles randomlyFor each frame

If |New frame-reference frame I > threshold)Foreground

End IfElse

BackgroundEnd Else

Calculate likelihoodMove particlesDisplay particlesEnd of for loop

End of Algorithm

A Particular Object Tracking in anEnvironment

of Multiple Moving Objects

• Background image initialization.

• Background subtraction.

• Background image update.

Flowchart

Background Image Update

Similarities b/w paper –I & paper-II

• We use background Subtraction algorithm.

• Use Particle Filter(For Tracking).

• Take Refernce Frames(For Detection).

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.

Continued....

• PF gives different values when there is color resolution.

• PF gives robust object tracking framework under ambiguity conditions.

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.