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

Image Processing Algorithms: Performance Evaluation

Acknowledgment C. Merry, G. Sharma, F. Lu,

M. McCord,

Past students: P. Goel, and J. Gardar

Vehicle Identification in High Resolution Satellite Imagery

• Infrequent Image Acquisition from satellites

• Stereo Coverage May be Unavailable

IKONOS Satellite Imagery: Tucson, AZ

Zooming-in

Image Segment for Processing

Zoomed and Pan Satellite Imagery (Columbus)

Problem Statement

• 1-m resolution image• 8 or 11-bit data• To detect and count

vehicles• Vehicle classes – cars

and trucks• No road detection

Pavement Background Image

• Lack of stereo Images

• Background (Pavement) Image

• No Background

• Background Based•Bayesian Background Transformation (BBT)•Principal Components (PCA)

•Gradient Based

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 classesInitial prior probabilities

Principal Components (PCA) Method

Principal Components Analysis

Binary Image

Vehicle counts

Roadway only Image (I) Background (B)

S = I + B

D = |I – B|

V1=Var2x2(S)

M1= Mean2x2(S) M2= Mean2x2(D)

V2=Var2x2(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

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

OriginalImage

Binary Image

Final Outcome

Simulated Images

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

• General Characteristics– Vehicles vs. pavement

• pavement type, vehicle color, atmospheric conditions

– Objects: Road signs, Lane markings– Road geometry– Traffic density

Real Image Test Cases

Image: I 75 – 1

Main Characteristic•Pavement material transition

Thresholded PC Band

Clustered Thresholded Gradient Img

Clustered

I 75 – 1

Probability Map Clustered

Probability Map

Image: I 75 – 2

•Pavement material transition

Thresholded PC Band

Clustered Thresholded Gradient

Img

Clustered

I 75 – 2

Probability Map

Clustered Probability

Map

Image: I 270 – 1•Pavement material transition•Overpass•Lane markings•Curved road segment

Thresholded PC Band Clustered

Thresholded Gradient Img

Clustered

I 270 – 1

Probability Map Clustered Probability Map

Image: I 270 – 2

Thresholded PC Band

Clustered

Thresholded Gradient Img

Clustered

•Lane markings•Pavement material transition•Straight segment•Fairly dense traffic

I 270 – 2

Probability Map

Clustered Probability Map

Image: I 70 – 1

Thresholded PC Band

Clustered

Thresholded Gradient Img

Clustered

•Lane markings•Sign board•Fairly dense traffic•Straight road segment

I 70 – 1

Probability Map

Clustered Probability Map

Image: I 10 – 1

•Straight road segment•Median•Good vehicle vs. pavement contrast

PC Band Thresholded… ClusteredGradient Img Thresholded… Clustered

I 10 – 1

Probability Map Clustered

Image: I 270 – 3

•Multiple pavement material transitions•Median•High traffic density

I 270 – 3

Image: I 71 – 1

•Poor vehicle vs. pavement contrast•Illumination change•Overpass

Thresholded PC Band

Clustered

I 71 – 1

Thresholded Gradient Img

ClusteredClustered Probability Map

I 71 – 1

Image: I 70 – 2

•Cloud cover•Overpass•Pavement material transition

I 70 – 2

Thresholded PC Band

Clustered

I 70 – 2

Thresholded Gradient Img

Clustered

I 70 – 2

Probability Map Clustered Probability Map

I 70 – 2

Results SummarySummary: 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

Summary

Future Needs

• Methods Not Requiring Background

• Post-processing

– sieving and clustering– Effort– Process