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A UAV Vision System for Airborne Surveillance

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.

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A UAV Vision System for Airborne Surveillance

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  1. 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

  2. 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

  3. Noise reduction Feature extraction (Size, Mean intensity) Feature vectors classification Alarm on/off Persistence Machine Vision System IR/NIR image

  4. Input Images • IR (3μm~ 14μm) • Near IR camera (1μm ~ 3μm) • 8bit grayscale

  5. Noise Reduction • 5x5 spatial Gaussian filter + Smoothes noise while preserving most of the features on the image

  6. Feature Extraction This module attempts to extract information about the regions on the image • Size of region using a region growing algorithm • Mean intensity of region defined as

  7. Feature Vector Classification Subsystem Mean Intensity of Region Fuzzy Classifier Target Identification Possibility Size of Region

  8. Low Mid High Grayscale values Mean Intensity Membership Functions

  9. Region Size Membership Functions Small Medium Large Pixels

  10. Objective ID Possibility Membership Functions

  11. Output of the Fuzzy Classifier Possibility Mean Intensity Size (pixels)

  12. Classification Example

  13. Classification Result p>0.8 0.5<p<0.8 p<0.5

  14. 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

  15. Alarm raising • Mechanism used : Alarm Registry • Ifpi > Ton => Activate alarm • Ifpi <Toff => Deactivate alarm

  16. 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

  17. Case Study: Forest fires Adjusting membership functions manually

  18. Classification Example (objective present) Thermal source (fire)

  19. Classification Result (objective present) possibility>0.7 0.5< possibility <0.7 possibility <0.5

  20. Classification Example (objective absent)

  21. Classification Result (objective absent) possibility>0.7 0.5< possibility <0.7 possibility <0.5

  22. Classification Result (Video)(objective present)

  23. Classification Result (Video) (objective absent)

  24. aij bij cij dij Automatic Parameter Selection • aijbijcijdijfori=1 andj =1,2,3 which define the form of the membership functions of Mean Intensity

  25. Basic Elements of the Genetic Algorithm • Chromosome => parameters x=(aijbijcijdij) • Fitness function correct activation of the alarm fitness(x)=1correct deactivation of the alarm fitness(x)=0 in any other case

  26. Basic Elements of the Genetic Algorithm • Selectionoperatorselects individualsfor matingas many times as the ratio of their fitness to the total fitness of the population • Crossover operatorcrossover probabilitypc=0.7 • Mutation operator mutation probabilitypm=0.001

  27. Mean Intensity M. F. as evolved by GA

  28. Result (using GA for parameter selection)

  29. 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)

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