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

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

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

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

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

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

7. SFTB Image provided by Night vision lab

8. SFTB 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

9. Images Node 1 Node 3 Node 2

10. Project Objective 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

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

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

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

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

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

16. Feature Extraction 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

17. Feature Extraction  1 =  20 +  02,  2 = (  20 -  02 ) 2 + 4  2 11  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 ] Central moments Normalized moments

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

19. Results 3 classes - 1, 2, 4; single scenario 7 features - 5 training vectors, 2 testing vectors k = 1, 3 Class112244 K=1222244 K=3121244 Class112244 K=1211444 K=3221444

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

21. Results Confusion matrix k=1124 1130 2121 4004 k=3124 1130 2211 4004

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

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

24. Thanks ?? and !!