AUTOMATIC TARGET RECOGNITION OF CIVILIAN TARGETS - PowerPoint PPT Presentation

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AUTOMATIC TARGET RECOGNITION OF CIVILIAN TARGETS

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  1. AUTOMATIC TARGET RECOGNITION OF CIVILIAN TARGETS September 28th, 2004 Bala Lakshminarayanan

  2. Objective Introduction to ATR Details of SFTB Database creation Segmentation Feature extraction, classification Results Conclusions Outline

  3. Civilian target classification Sensor fusion SFTB objectives - Generation of dataset for ATR - Ground truth data collection Objective

  4. What is ATR Why do we need it Types of ATR - Aided, unaided - Binary, multi-valued Problems Introduction to ATR

  5. Requirements - Real time operation - Low false positives - High detection rates Applications - Military - Medical - Industrial Introduction to ATR

  6. Nodes - Base station - 2 with IR sensor - 1 with visible light sensor Node placement Targets (cars, light trucks, SUVs) Ground truth collection equipment Scenarios SFTB

  7. SFTB Image provided by Night vision lab

  8. Fully exposed targets except by other presence on scene Stationary sensors Daylight operation License plates not readable Constant velocity/acceleration Different scenarios (3) Simultaneous data capture SFTB

  9. Images Node 1 Node 3 Node 2

  10. Use IR and visual images to classify targets Use sensor fusion to improve accuracy Creation of image database Creation of framework Segmentation, feature extraction, classification Project Objective

  11. Images in .arf files Use frames captured at same time “Event start” - Range from Node2 = 20 “Event end” - Outside FoV of Node3 Database Creation

  12. Framework Inputs-nodeID, scenario… Start Grab frame from dataset filename() Segment bgSubtract(), motionDet() Extract features invMoment() Classify readData(), knn() End

  13. Used to identify the target/RoI in the frame Methods - Thresholding - Background subtraction - Motion based segmentation Segmentation

  14. Background subtraction median(frame)-median(background) Noise removal by neighbourhood() Segmentation - = - =

  15. Motion based segmentation temp1=average(prev)-average(frame) temp2=average(next)-average(frame) temp1&temp2 Segmentation

  16. Features should describe similar targets similarly Seven invariant moments (Hu, 1962) Computed from central moments, third order Translational invariance – C.G Distance invariance – Size normalization Feature Extraction

  17. Feature Extraction Central moments Normalized moments 1 = 20 + 02, 2= (20 - 02)2 + 4211 3 = (30 - 312)2 + (03 - 321)2, 4 = (30 + 12)2 + (03 + 21)2 5 = (330 - 312)(30 + 12)[(30 + 12)2 –3(21 + 03)2] + (321 - 03)(21 + 03)  [3(30 + 12)2 – (21 + 03)2] 6 = (20 - 02)[(30 + 12)2 – (21 + 03)2] + 411(30 + 12)(21 + 03) 7= (321 - 03)(30 + 12)[(30 + 12)2 - 3(21 + 03)2] + (312 - 30)(21 + 03)  [3(30 + 12)2 – (21 + 30)2]

  18. Supervised or unsupervised k-nearest neighbour method Training vectors are given Find k nearest neighbours, maximum presence Classification

  19. 3 classes - 1, 2, 4; single scenario 7 features - 5 training vectors, 2 testing vectors k = 1, 3 Results

  20. Overall classification results k=1 – 58.33% k=3 – 50% Target1 – 25% Target2 – 38.5% Target4 – 100% Results

  21. Confusion matrix Results

  22. Database created Basic framework has been laid Robust segmentation needed More training vectors Segmentation does not work for px files Conclusions

  23. Segmentation - Quadtree based split-merge - Use of Kalman filters - Histogram based segmentation Better features need to be used Future work

  24. Thanks ?? and !!