a uav vision system for airborne surveillance

30
A UAV Vision System for Airborne Surveillance M.Kontitsis, K. Valavanis IEEE 2004 International Conference on Robotics and Automation Technical University of Crete University of South Florida N. Tsourveloudis

Upload: natane

Post on 19-Jan-2016

64 views

Category:

Documents


0 download

DESCRIPTION

IEEE 2004 International Conference on Robotics and Automation. A UAV Vision System for Airborne Surveillance. N. Tsourveloudis. M.Kontitsis, K. Valavanis. University of South Florida. Technical University of Crete. Objectives. - PowerPoint PPT Presentation

TRANSCRIPT

Page 1: A UAV Vision System for Airborne Surveillance

A UAV Vision System for Airborne Surveillance

M.Kontitsis, K. Valavanis

IEEE 2004 International Conference on Robotics and Automation

Technical University of Crete

University of South Florida

N. Tsourveloudis

Page 2: A UAV Vision System for Airborne Surveillance

Objectives

• Present a methodology for the design of a machine vision system for aerial surveillance by Unmanned Aerial Vehicles (UAVs)

• Identify specified thermal source• Perform these functions on board the

UAV in Real time• Flexible enough to be used in a variety

of applications

Page 3: A UAV Vision System for Airborne Surveillance

Machine Vision System

IR/NIR image Noise reduction

Feature extraction

(Size, Mean intensity)

Feature vectors classification

Alarm on/off

Persistence

Page 4: A UAV Vision System for Airborne Surveillance

Input Images

IR (3μm ~ 14μm)

8bit grayscale

Near IR camera (1μm ~ 3μm)

Page 5: A UAV Vision System for Airborne Surveillance

Noise Reduction

5x5 spatial Gaussian filter

+ Smoothes noise while preserving most of the features on the image

Page 6: A UAV Vision System for Airborne Surveillance

Feature Extraction

Size of region using a region growing algorithm

Mean intensity of region defined as

This module attempts to extract information about the regions on the image

Page 7: A UAV Vision System for Airborne Surveillance

Feature Vector Classification Subsystem

Mean Intensity of Region

Size of Region

Target Identification Possibility

Fuzzy Classifier

Page 8: A UAV Vision System for Airborne Surveillance

Mean Intensity Membership Functions

Grayscale values

HighMidLow

Page 9: A UAV Vision System for Airborne Surveillance

Region Size Membership Functions

Pixels

Small Medium Large

Page 10: A UAV Vision System for Airborne Surveillance

Objective ID Possibility Membership Functions

Page 11: A UAV Vision System for Airborne Surveillance

Output of the Fuzzy Classifier

Page 12: A UAV Vision System for Airborne Surveillance

Classification Example

Page 13: A UAV Vision System for Airborne Surveillance

Classification Result

p>0.8

0.5<p<0.8

p<0.5

Page 14: A UAV Vision System for Airborne Surveillance

Alarm raising

Persistent classification of a certain region as of High Possibility raises the alarm

The region that raised the alarm is pin-pointed by a red cross

The alarm stays on even if the thermal source is temporarily occluded by surroundings or lost due to violent camera vibrations

Page 15: A UAV Vision System for Airborne Surveillance

Alarm raising

Mechanism used : Alarm Registry

Region Coordinates Variable persistancei1,j1 p1

i2,j2 p2

…. ……..

in,jn pn

If pi > Ton => Activate alarm

If pi < Toff => Deactivate alarm

Page 16: A UAV Vision System for Airborne Surveillance

Complexity

Noise Reduction O(n2) for (nxn) image

Region Growing O(n2) for (nxn) image

Fuzzy Logic Classifier* O(nxm)

*in its current implementation

n inputs, m rules

Page 17: A UAV Vision System for Airborne Surveillance

Case Study: Forest fires

Adjusting membership functions manually

Page 18: A UAV Vision System for Airborne Surveillance

Classification Example (objective present)

Thermal source (fire)

Page 19: A UAV Vision System for Airborne Surveillance

Classification Result (objective present)

possibility>0.7 0.5< possibility <0.7 possibility <0.5

Page 20: A UAV Vision System for Airborne Surveillance

Classification Example (objective absent)

Page 21: A UAV Vision System for Airborne Surveillance

Classification Result (objective absent)

possibility>0.7 0.5< possibility <0.7 possibility <0.5

Page 22: A UAV Vision System for Airborne Surveillance

Classification Result (Video) (objective present)

Page 23: A UAV Vision System for Airborne Surveillance

Classification Result (Video) (objective absent)

Page 24: A UAV Vision System for Airborne Surveillance

Automatic Parameter Selection

aij bij cij dij for i=1 and j =1,2,3 which define the form of the membership functions of Mean Intensity

aijbij cij dij

Page 25: A UAV Vision System for Airborne Surveillance

Basic Elements of the Genetic Algorithm

Fitness function

fitness(x)=1 correct deactivation of the alarm

fitness(x)=0 in any other case

correct activation of the alarm

Chromosome => parameters x=(aij bij cij dij)

Page 26: A UAV Vision System for Airborne Surveillance

Basic Elements of the Genetic Algorithm

Selection operator selects individuals for mating as many times as the ratio of their fitness to the total fitness of the population

Crossover operator crossover probability pc=0.7

Mutation operator mutation probability pm=0.001

Page 27: A UAV Vision System for Airborne Surveillance

Mean Intensity M. F. as evolved by GA

Page 28: A UAV Vision System for Airborne Surveillance

Result (using GA for parameter selection)

Page 29: A UAV Vision System for Airborne Surveillance

Remarks

Adjustable for a variety of applications

Real time execution

Correct identification rate of about 90%

False alarms not entirely avoided (especially in the system evolved by the GA)

Page 30: A UAV Vision System for Airborne Surveillance