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Pedestrian Detection using Infrared Imagesand Histograms of Oriented Gradients

F. Suard1, A. Rakotomamonjy1, A. Bensrhair1, A. Broggi2

frederic.suard@insa-rouen.fr

1

Laboratoire dInformatique, Traitement de lInformation, Systemes.INSA de Rouen, Rouen, France

2 Dipartimento di Ingegneria dellInformazione,Universita di Parma, Parma, Italy

Intelligent Vehicle Symposium 2006

Tokyo, 14th June 2006

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Introduction HOG Method Application Conclusion

Introduction

Machine learning and vision system.Histogram of Oriented Gradient [DT05],

Classifier : Support Vector Machines [Vap98].

Application : pedestrian detection with infrared images.

Objectives

using HOG method for pedestrian detection,

extracting windows from infrared images.F. Suard 2

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Introduction HOG Method Application Conclusion

Histogram of Oriented Gradient

Introduced by N. Dalal and B. Triggs [DT05] representing an image (128 64 pixels) with a vector.

Computation of local gradient histograms.

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Introduction HOG Method Application Conclusion

HOG computation steps

Original Image :

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HOG computation steps

Gradient Orientation and Norm:

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Introduction HOG Method Application Conclusion

HOG computation steps

Cell Splitting:

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Introduction HOG Method Application Conclusion

HOG computation steps

Histogramm normalization, block 1:

Final descriptor: [0.01 0.5 0 0.8]

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Introduction HOG Method Application Conclusion

HOG computation steps

Histogramm normalization, block 2:

Final descriptor: [0.01 0.5 0 0.8 0.2 0 0.9 0]

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Introduction HOG Method Application Conclusion

HOG computation steps

Histogramm normalization, block n:

Final descriptor: [0.01 0.5 0 0.8 0.2 0 0.9 0 ... 0.6 0.7 0.1 0]

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Introduction HOG Method Application Conclusion

HOG parameters

Parameterscell: number of pixels,

block: number of cells, overlap, normalization factor (no, L1, L2),

histogram: number of bins, weighted vote(gradient magnitude, vote).

Exhaustive test for parameters tuning,

Dataset : pedestrians and non-pedestrians manually extracted.

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Introduction HOG Method Application Conclusion

HOG parameters

Optimal set of parameters

size of cell : 8 8 pixels,

size of block : 2 2 cells,

overlap between blocks : 1 cell,normalization factor for block : L2,

number of bins per histogram : 8

weigthed vote for histogram : gradient magnitude.

Descriptor dimension: 3360

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Introduction HOG Method Application Conclusion

Linear SVM Classifier

Data X Rn

Label y {1, 1}

Decision function f(x) =m

k=1 k yk xk, x + b

Class ofX = sign of f(x)

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Introduction HOG Method Application Conclusion

Single Frame Classification

ROC Curve for single frame classifi-cation (test dataset: 4400 examples)when size of learning dataset varies :

Confusion matrix (1000)

TrueP N

PredictionP 2096 54

N 71 2079

detection 0.9749

accuracy 0.9709

precision 0.9672

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Introduction HOG Method Application Conclusion

Single Frame Classification

ROC Curve for single frame classifi-cation (test dataset: 4400 examples)when size of learning dataset varies :

Confusion matrix (1000)

True

P N

PredictionP 2096 54

N 71 2079

detection 0.9749

accuracy 0.9709

precision 0.9672

For 90 % of good recognition : 1 false-positive for330 windows examined

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Introduction HOG Method Application Conclusion

Misclassification

Examples of bad classification :

mis-classification

mis-classification

false-positive false-positive

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Introduction HOG Method Application Conclusion

HOG applied to infrared images

Application

infrared images,

pedestrian detection.

Windows extraction function

Particularity of infrared images

Warm area (pedestrian head) appears lighter

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Introduction HOG Method Application Conclusion

Windows extraction

Original Image:

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Windows extraction

Warm areas:

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Windows extraction

Warm area of the second pedestrian:

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I d i HOG M h d A li i C l i

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Windows extraction

Gradient :

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I t d ti HOG M th d A li ti C l i

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Windows extraction

left and right bounds:

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Windows extraction

upper bound:

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Windows extraction

lower bounds:

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Windows extraction

combination and windows extraction (> 1000):

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Introduction HOG Method Application Conclusion

Windows extraction

Classification:

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pp

Results

Windows which prediction are over threshold :

f(x) > 0 f(x) > 0.5

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pp

Results

f(x) > 0 f(x) > 0.5

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Results

f(x) > 0 f(x) > 0.5

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Results

f(x) > 0 f(x) > 0.5

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Results

f(x) > 0 f(x) > 0.5

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Conclusion and perspectives

+Good results for single frame classification,

Answer for pedestrian size variability,

Good generalization for pedestrian pose variability.

-

Parameters,

Windows extraction.

PerspectivesImprove performance,

reduce computation time,

work with sequences.

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References

Navneet Dalal and Bill Triggs.

Histograms of oriented gradients for human detection.In Cordelia Schmid, Stefano Soatto, and Carlo Tomasi, editors, International Conference on ComputerVision and Pattern Recognition, volume 2, pages 886893, INRIA Rhone-Alpes, ZIRST-655, av. delEurope, Montbonnot-38334, June 2005.

A. Broggi A. Fascioli P. Grisleri T. Graf M. Meinecke.

Model-based validation approaches and matching techniques for automotive vision based pedestriandetection.In Intl. IEEE Wks. on Object Tracking and Classification in and Beyond the Visible Spectrum, San Diego,USA, page in press, June 2005.

V. Vapnik.

Statistical Learning Theory.

Wiley, 1998.

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