visual object tracking using particle clustering - icitacee 2014

VISUAL OBJECT TRACKING

USING PARTICLE CLUSTERING

Harindra W Pradhana

wisnu@intiteknologi.co.id

Content Summary

Key Purpose

Image Processing

Water level model

Color feature

New approach

Object Tracking

Pixel matching

Pixel clustering

Localization & tracking

Performance Analysis

Speed

Accuracy

Conclusions

A little BIT

Key Purpose

Object Tracking

Location relatively from the observer

Low-cost vision sensor

Low resolution

Limited frame rate

Image noise

Effective result

Image Processing

Image Processing – Water level model

Use brightness level

Detects hill and valley

(Harris, 1988)

Image Processing – color features

(Bretzner, 2002)

Use skin color as reference

Extract and calculate color features from RGB

components

Find similar color

Image Processing – New approach

Use both physical space & color space

Both space Euclidean distance measured for clustering

Introduce new color features

Eliminate brightness level better with webcam

Add 255 constants avoid similar distance value on different color

GR+=f 2551

BR+=f 2552

BG+=f 2553

Object Tracking

Object Tracking – Pixels matching

23

2

2

2

1 df+df+df=qp,d

Given particular

threshold

Calculate color

distance

Preview match pixels

Object Tracking – CF comparison

Colour

Green New CF

Orange both

Red Bretzner’s CF

Better detection

Adaptable in poor

lightning

Capable detecting

shiny surface

Object Tracking – Pixels clustering

Given particular

threshold

Count matching pixel

Decide good cluster

Preview the cluster

Object Tracking – Localization &

Tracking

Point out good pixels

in good clusters

Calculate center

weight

Keep last position

Calculate movement

Predict future position

Performance Analysis

Performance Analysis – Speed

Best result :

Resolution =

640x480

Frame rate =

30fps

Block size > 10p

Processing time =

33ms/frame

Performance Analysis – Effectiveness

Higher good

to bad pixel

detection ratio

compared to

Bretzner’s CF

Conclussion

Successfully locate object and track their movement

Clustering produce faster frame processing by

lowering computation size

New color feature effectively increase particle

detection

Capable to adapt poor lighting area by eliminating

brightness component

A Little BIT

BIT – IntiTeknologi

info@intiteknologi.co.id

http://intiteknologi.co.id