video tracking system based on computer vision

8/12/2019 Video Tracking System Based on Computer Vision

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VIDEO TRACKING SYSTEM

BASED ON COMPUTER VISION

MASTER DOMAIN: INFORMATION

PROCESSING IN COMMUNICATIONSAND MULTIMEDIA

MASTERS DEGREE PROJECT


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SUMMARY

1. INTRODUCTION

2. ANALYSIS

3. IMPLEMENTING

4. TESTING AND EXPERIMENTAL RESULTS

5. CONCLUSIONS


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1. INTRODUCTION

1.1 Context and motivation BIOMETRICS - a relatively new connotation

The human face recognition

1.2 General outlook

Potential detection solutions:1. using a single colour background

2. searching a particular area of colour

3. working with an image in different shades of grey

4. the extraction of the backgroundPotential tracking solutions:1. background extraction

2. pixel temporal differentiation

3. optical flow method


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2. ANALYSIS

The main conditions for the detection solution: detection independence detection must be done also in the case of noisy images detection should be fast and repeated during tracking.

The conditions for the tracking algorithm:

least influence by unclear image due to motion blur or on falsedetection solving possible background differences from one frame to

another ability to operate during different light intensity not being influenced by background changes or displacements must to follow the detected face in a center-stage position,

without being influenced by other faces detected in thebackground area

must to offer the possibility to compress the images as videofiles


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Human face detection

Diagram representation

of a detection cascade

The first two features

selected by the

AdaBoost algorithm


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Face tracking process

It is proposed that the tracking process to be performedthrough foreground face detection.

r2

P2(x2,y2)

rn

Pn(xn,yn)

r1

r0

P1(x1,y1)

y

x

dn

d2

d1

P0(x0,y0)

C(X,Y)

P(x,y)

y

x

Center of

the image

To resolve multiple detection

case, there are two

proposed selection criteria:

Maximum face area

r = max(r1, r2, ... , rn)

Minimum distance between the mass

center of the detected face within the

previous stage and the mass center of

the faces detected in the current stage

d = min(d1, d2, ... , dn)


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Communication

The proposed

system is aimed atdetecting andtracking faces fromdistance, from theInternet.

Such a system isendowed with thefeatures of twosystems already

known, that are: human-machine

interfaces

the IP cameras


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Hardware


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Disigning the User interface

User interface and the capture and tracking module, as well as

microcontroller operation mode - Command operation module

main

topLeft

middleLeft

Menu

Commands

middleRight

Vertical

Commands

topMiddle topRight

middle

middleMiddle

Video Content

middleBottom

Horizontal Commands

Video Capture & Tracking

Command ModuleSerial Command Module


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Designing the video processingsystem

mencoderconvert(ImageMagic)

JPEG

AVI

BMP

BMP

BMP JPEG

JPEG

JPEGcompression

MPEGcompression

videocapture

OpenCV

1. INPUT

2. MAIN

3. LOOP

4. DETECT & DRAW

5. TIMING

START

STOP

The program was splitted in blocks,according to the type of performedoperations

Stages of image processing in the face tracking system


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TESTING AND EXPERIMENTALRESULTS

Determining the maximum speed of a face in the detection

process

Hitec HS-300 servo specifications:

speed s= 60 degrees /0.19 s

step= 0.36 degrees

= /(t + t)

where t = /s

rr

X = L 2r

L

The diminished space

X = L-2r

= (L-2r)A/L

= /tv tv= /

tv = time of view

A

rr L-2r

t t


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Graphical representation of the frame rate

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Input Frame Rate

Angular Speed

Average Frame Rate


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-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

FPS

DetectionTime

Detections

False detections

Graphical representation of a test (20 sec) for the angular speed of

0.389293 rad/sec (applied delay: 15 milliseconds/period)


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video tracking system based on computer vision

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