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Semantic Geometric Features: A Preliminary Investigation of Automobile

Identification

Carl E. AbramsSung-Hyuk Cha, Michael Gargano, and Charles

Tappert

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Agenda

• Overview of the Problem

• The Experiments

• Results

• Going Forward

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Overview

• Object recognition remains a hard problem

• The human mind uses shapes to recognize objects

• Can semantic features defined by their shapes be more effective in the recognition and identification of objects?

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The Experiments

• 10 test images of cars

• Directly form the manufactures websites

• Images were restricted to side views of the cars taken from 90 degrees

• All 2005 models

• Feature vectors calculated/measured from the images

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The Vehicles

Honda Accord Sedan 2005 Honda Civic Coupe 2005 Mazda 3 2005

Mazda 6 2005 Porsche Carrera Toyota Camry 2005

Toyota Corolla CE 2005 Toyota Celica GT 2005 Toyota Echo 2005

VW Passat 2005

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Experiments used Euclidean Distance as the Measure

L

iii txd

1

2)(

the xi and ti are measurements from two different vehicles

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Experiments used Euclidean Distance as the Measure

(x1,y1)

(x2,y2)c

a

b c = (a2+b2)1/2

c = ((x1-x2)2+(y1-y2)2)1/2

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Manufacturers SpecificationsFirst Experiment

Vehicle Description Wheelbase in inches Width Length Height Honda Accord Civic Coupe 2005 103.1 66.7 175.4 55.1 Honda Accord Sedan 2005 107.9 71.5 189.5 57.1 Mazda 3 2005 103.9 69.9 178.3 57.7 Mazda 6 2005 105.3 70.1 186.8 56.7 Porsche Carerra 2005 92.52 71.18 174.29 51.57 Toyota Camry 2005 107.1 70.7 189.2 58.7 Toyota Celica GT 2005 102.4 68.3 170.5 51.4 Toyota Corolla CE 2005 102.4 66.9 178.3 58.5 Toyota Echo 2005 92.3 65.4 164.8 59.4 VW Passat 2005 106.4 68.7 185.2 57.6

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Boundary Description using RaysSecond Experiment

Ray length in mm at angles in degress

Vehicle Description Wheelbase in mm 5 10 15 20 25 30 35 40 45 50 55 60Honda Accord Civic Coupe 2005 148 125 121 123 110 95 90 86 81 78 75 70 66Honda Accord Sedan 2005 139 120 116 118 110 98 90 85 80 78 73 70 67Mazda 3 2005 146 118 113 115 113 100 94 89 84 80 78 71 67Mazda 6 2005 140 118 104 115 109 95 88 84 80 76 72 69 63Mercedes Benz C230 Sports Coupe 2005 138 102 115 112 115 100 94 89 82 79 73 71 67Nissan Altima 2005 136 115 110 110 110 91 86 81 76 71 66 63 62Porsche Carerra 2005 128 112 112 115 100 90 82 76 71 67 66 62 60Toyota Camry 2005 136 126 127 126 120 101 94 88 86 73 73 71 68Toyota Celica GT 2005 134 102 104 111 113 87 82 75 68 65 63 60 57Toyota Corolla CE 2005 144 119 117 120 113 96 93 89 84 82 77 73 69Toyota Echo 2005 154 125 124 127 112 113 108 100 99 96 92 87 84VW Passat 2005 145 122 120 120 110 96 90 86 82 74 72 69 66

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Semantic FeaturesThird Experiment

Vehicle Description Wheelbase in mm Angle FD Length FD Angle RD Length RD Angle Mirror Length MirrorHonda Accord Civic

Coupe 2005 148 123 33 0 0 51 50Honda Accord Sedan 2005 139 98 28 154 63 51 47

Mazda 3 2005 146 92 30 150 58 51 50Mazda 6 2005 140 93 29 151 64 47 48

Mercedes Benz C230 Sports Coupe

2005 138 126 37 0 0 52 43

Nissan Altima 2005 136 98 25 153 58 47 45Porsche Carerra

2005 128 144 33 0 0 69 34

Toyota Camry 2005 136 85 24 151 62 42 53Toyota Celica GT

2005 134 125 31 0 0 49 44Toyota Corolla CE

2005 144 90 34 148 63 42 58Toyota Echo 2005 154 97 34 0 0 49 65VW Passat 2005 145 97 36 154 59 47 50

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Challenge: Determine the qualitative ability of the feature vectors to separate the vehicles

• Within each experiment compute the distance of each vehicle from all the others

• Evenly divide the measures into 5 bins

• Observe the distribution of the measures

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The Results

05101520253035404550

Bin1 Bin2 Bin3 Bin4 Bin5

HWD

Semantic

Rays

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Distance Matrix – Semantic Features

0 156.02 153.17 154.00 27.66 155.96 2.82 151.90 26.07 156.23

156.02 0 7.21 7.07 161.72 16.09 156.36 13.45 154.01 4.12

153.17 7.21 0 4.24 159.78 11.44 153.60 9.43 150.09 7.55

154.00 7.07 4.24 0 160.89 9.43 154.37 6.55 151.06 5.00

27.65 161.73 159.77 160.89 0 164/35 27.58 159.84 51.08 162.50

155.96 16.09 11.44 9.43 164.35 0 156.36 5.83 151.63 13.34

2.82 156.36 153.60 154.36 27.58 156.36 0 152.24 28.00 156.53

151.90 13,45 9.43 6.55 159.84 5.83 152.24 0 148.33 10.48

26.07 154.01 150.09 151.06 51.07 151.63 28.00 148.33 0 154.01

156.23 4.12 7.55 5.00 162.50 13.34 156.53 10.48 154.01 0

Honda Civic

Honda Accord

Mazda 3 Mazda 6 Porsche Carerra

Toyota Camry

Toyota Celica

Toyota Corolla

Toyota Echo

VW Passat

Honda Civic

Honda Accord

Mazda 3

Mazda 6

Porsche Carerra

Toyota Camry

Toyota Celica

Toyota Corolla

Toyota Echo

VW Passat

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Going Forward

• Extend techniques to encompass semantic shapes within an object (shape contexts)

• Compare the extended semantic methods to existing methods in multiple domains

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Going Forward

Shape Contexts

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References

• [1] R. D. Acqua and R. Job, "Is global shape sufficient for automatic object identification?" Congitive Science, vol. 8, pp. 801-821, 2001.

• [2] A. K. Jain, A. Ross, and S. Pankanti, "A Prototype Hand Geomtery-based Verification System," presented at Proceedings of 2nd International conference on Audio and Video-based Biometric Person Authentication, Wahington D.C., 1999.

• [3] H. Schneiderman and T. Kanade, "A Statistical Model for 3D Object Detection Applied to Faces and Cars," presented at IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2000

• [4] S. Belongie,J Malik, J Puzicha, “Matching Shapes” ,presented at the International Conference on Computer Vision (ICCV 01) Vol 1, Jan 2001