Visualization And Characterization of Real Life Motion

Presented By:Abu Farzan Mitul

Roll:0603020&

Muhammad RakeebRoll:0603039

Supervised By:Dr. Md. ShahjahanAssociate ProfessorDept. of Electrical & Electronic EngineeringKhulna University of Engineering & Technology

Department of Electrical & Electronic Engineering

Khulna University of Engineering & Technology

OutlineVision of WorkPrevious WorkSystem Architecture

Moving Object TrackingChaos AnalysisObject Classification

Future WorkConclusion

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Vision of Work

To Track a Moving Object in Real Time.

To Evaluate the Motion of Object in Chaos Analysis.

To Classify Different Objects according to their Motion.

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Previous WorkMoving Object Tracking

Multiple Features (e.g. Color, Shape, Motion, Edge etc.) were used

In Case of Chaos Analysis The Motion of Magneto tactic Bacterium was shown

Chaotic.Motion of the fish was shown Chaotic using Lorenz

Chaos Equation etc.In Case of Object Classification

A FFNN was utilized to distinguish between human and Vehicle.

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System Architecture

Object TrackingModule

Chaos Analysi

s

Data Storage

Object Classificat

ionusingNeural Network

Hurst Exponent

Lyapunov Exponent

Correlation

Dimension

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1. Moving Object TrackingBased on the following Steps…

Image Capturing Module

Image Processing Server (IPS)

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Image Capturing Module

Consist of a Static Web-Cam.

Captures image stream in .jpg format

Frame size of 320×240

Passes the Image stream to the IPS

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Image Processing Server (IPS)Functions of the IPS are…

To Receive Image Stream from Web CamTo Process the Image & Track ObjectTo Generate The Time Series of the Object

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Tracking Algorithm

BackgroundSubtraction

Gray Scaling

Binarizing

Deleted

Ref. Image

Image Stream

(Filtered)Image Stream

(Black & White)

NextFrame

Image Stream(Binariz

ed)

Webcam

1st Frame

2nd Frame

Object Location Detected

Image Stream(Color)

Euclidean

Color Filtering

Data Storage

Time Series 9

Objects Taken for Analysis

Common Name Scientific Name Defined Class

Angle Fish Pteropthyllum scalare Class 1

Siamses Fighter Fish Betta splendens Class 2

Black Pony Gold Fish Carassius auratus Class 3

3 Different Fishes are taken in Our Analysis

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Visual Presentation

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2. Chaos Analysis• What Is Chaos?

– An Irregular Behavior of Dynamic System.

• Why Chaos Analysis?– To Study Complex Dynamical Behavior(e.g. Motion of Living Beings)

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Methods for Analysis

– Auto Correlation– Hurst Exponent– Lyapunov Exponent– Correlation Dimension– Complexity etc.

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Methods UsedHurst Exponent

• 0 ≤ HE<0.5 (Non-Chaotic )• 0.5 < HE ≤ 1 (Chaotic)

Lyapunov Exponent• Positive Value of Exponent indicates Chaos

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Analytical ResultsFor Class 1

Minimum HE = 0.5849 (≥ 0.5)For Class 2

Minimum HE = 0.7431 (≥ 0.5)For Class 3

Minimum HE = 0.7135 (≥ 0.5)

Chaotic Nature in Motion of These fishes is Proved

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3. Object Classification

Object TrackingModule

Time Series

Classifier

ObjectRecognition

Data Storage

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ANN as Classifier• What Is ANN?

– A Computational Method– Inspired from nervous System of biological Organisms

• Why ANN?– Advanced – Commonly Used– More Tolerance to Noise Input– Allows Supervised Learning

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Artificial Neural Network

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Time Series

Neural Network including

Connections (Called weights) betn

Neurons

Compare

Target

Output

Adjust Weights

Visual Presentation

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Performance Analysis

Class 1Class 2

Class 3

93.33%86.66%

73.33%

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Performance Improvement

Larger Time SeriesMore Training Data for Each Class

Faster Frame Sequence

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Future Work

Chaos Analysis using…– Lyapunov Exponent– Correlation Dimension

Improvement of NN performance using…– Multiple Camera

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Conclusion• Simulation results show that the proposed system is successful in tracking the moving fishes and in classifying them

• This system can be implemented on every real life motions

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Thank You All