1. Vehicle Detection with Satellite Images Presented by Prem K. Goel NCRST-F, The Ohio State University Workshop on Satellite Based Traffic Measurement Berlin, Germany 9-10 September 2002

2. Image Processing Algorithms: Performance Evaluation Acknowledgment C. Merry, G. Sharma, F. Lu, M. McCord, Past students: P. Goel, and J. Gardar

3. Vehicle Identification in High Resolution Satellite Imagery Infrequent Image Acquisition from satellites Stereo Coverage May be Unavailable

4. IKONOS Satellite Imagery: Tucson, AZ

5. Zooming-in

8. Image Segment for Processing

9. Zoomed and Pan Satellite Imagery (Columbus)

10. Problem Statement 1-m resolution image 8 or 11-bit data To detect and count vehicles Vehicle classes – cars and trucks No road detection

11. Pavement Background Image Lack of stereo Images Background (Pavement) Image No Background Background Based Bayesian Background Transformation (BBT) Principal Components (PCA) Gradient Based

12. BBT Method: Flow Chart Update probabilities Highway Image (I) Background (B) Background Transform Estimate Distribution Parameters Threshold Clustering and other operations Vehicle Counts Converged? Yes No Estimate probability of a pixel being stationary based on change from background Distributions of gray-levels in two classes Initial prior probabilities

13. Principal Components (PCA) Method Principal Components Analysis Binary Image Vehicle counts Roadway only Image (I) Background (B) S = I + B D = |I – B| V 1 =Var 2x2 (S) M 1 = Mean 2x2 (S) M 2 = Mean 2x2 (D) V 2 =Var 2x2 ( D) Select PC Band. Threshold Clustering and other operations PC Bands 1-4 PCA-based Method Bands to capture texture and change Re-orient bands

14. Segmented Highway Image (I) Calculate Gradient Image Threshold Morphological operations and Clustering Vehicle counts Gradient Based Method The ‘edge’ at vehicle boundaries Gradient image = image with two classes Threshold -try to incorporate spatial distribution of gray values Gradient based method

15. Original Image Binary Image Final Outcome

16. Simulated Images No Method was best Different method performed well for different images Performance Evaluation on Real Images crucial

17. General Characteristics –Vehicles vs. pavement pavement type, vehicle color, atmospheric conditions –Objects: Road signs, Lane markings –Road geometry –Traffic density Real Image Test Cases

18. Image: I 75 – 1 Main Characteristic Pavement material transition Thresholded PC Band Clustered Thresholded Gradient Img Clustered

19. I 75 – 1 Probability Map Clustered Probability Map

20. Image: I 75 – 2 Pavement material transition Thresholded PC Band Clustered Thresholded Gradient Img Clustered

21. I 75 – 2 Probability Map Clustered Probability Map

22. Image: I 270 – 1 Pavement material transition Overpass Lane markings Curved road segment Thresholded PC BandClustered Thresholded Gradient Img Clustered

23. I 270 – 1 Probability Map Clustered Probability Map

24. Image: I 270 – 2 Thresholded PC Band Clustered Thresholded Gradient Img Clustered Lane markings Pavement material transition Straight segment Fairly dense traffic

25. I 270 – 2 Probability Map Clustered Probability Map

26. Image: I 70 – 1 Thresholded PC Band Clustered Thresholded Gradient Img Clustered Lane markings Sign board Fairly dense traffic Straight road segment

27. I 70 – 1 Probability Map Clustered Probability Map

28. Image: I 10 – 1 Straight road segment Median Good vehicle vs. pavement contrast PC Band Thresholded… Clustered Gradient Img Thresholded… Clustered

29. I 10 – 1 Probability Map Clustered

30. Image: I 270 – 3 Multiple pavement material transitions Median High traffic density

31. I 270 – 3

32. Image: I 71 – 1 Poor vehicle vs. pavement contrast Illumination change Overpass Thresholded PC BandClustered

33. I 71 – 1 Thresholded Gradient Img Clustered Probability Map

34. I 71 – 1

35. Image: I 70 – 2 Cloud cover Overpass Pavement material transition

36. I 70 – 2 Thresholded PC Band Clustered

37. I 70 – 2 Thresholded Gradient Img Clustered

38. I 70 – 2 Probability Map Clustered Probability Map

39. I 70 – 2

40. Results Summary Summary: Errors of Omission and Commission BBT and gradient method give numbers close to the real values Large errors of omission and commission for PCA and gradient based method Low omission and commission errors for BBT method

41. Summary

42. Future Needs Methods Not Requiring Background Post-processing – sieving and clustering –Effort –Process