Vehicle Detection with Satellite Images

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

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

13. Background (B) Roadway only Image (I) S = I + B D = |I – B| V1=Var2x2(S) V2=Var2x2(D) M2= Mean2x2(D) M1= Mean2x2(S) Principal Components Analysis PC Bands 1-4 Select PC Band. Threshold Binary Image Clustering and other operations Vehicle counts Principal Components (PCA) Method • 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 • Gradient Based Method • The ‘edge’ at vehicle boundaries • Gradient image = image with two classes • Threshold • try to incorporate spatial distribution of gray values

15. Final Outcome Original Image Binary Image

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

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

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

19. I 75 – 1 Probability Map Clustered Probability Map

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

21. I 75 – 2 Clustered Probability Map Probability Map

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

23. I 270 – 1 Clustered Probability Map Probability Map

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

25. I 270 – 2 Probability Map Clustered Probability Map

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

27. I 70 – 1 Probability Map Clustered Probability Map

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

29. I 10 – 1 Clustered Probability Map

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

31. I 270 – 3

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

33. I 71 – 1 Clustered 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 Clustered Probability Map 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