Pedestrian Detection using Infrared Images and Histograms of
Transcript of Pedestrian Detection using Infrared Images and Histograms of
Pedestrian Detection using Infrared Imagesand Histograms of Oriented Gradients
F. Suard1, A. Rakotomamonjy1, A. Bensrhair1, A. Broggi2
1 Laboratoire d’Informatique, Traitement de l’Information, Systemes.INSA de Rouen, Rouen, France
2 Dipartimento di Ingegneria dell’Informazione,Universita di Parma, Parma, Italy
Intelligent Vehicle Symposium 2006
Tokyo, 14th June 2006
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.
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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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HOG computation steps
Original Image :
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HOG computation steps
Gradient Orientation and Norm:
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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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HOG parameters
Parameters
cell: 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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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) =
∑mk=1 αk · yk · 〈xk , x〉+ b
Class of X = 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 54N 71 2079
detection 0.9749accuracy 0.9709precision 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)
TrueP N
PredictionP 2096 54N 71 2079
detection 0.9749accuracy 0.9709precision 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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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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Windows extraction
Gradient :
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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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Windows extraction
Classification:
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Results
Windows which prediction are over threshold :
f (x) > 0 f (x) > 0.5
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Results
f (x) > 0 f (x) > 0.5
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Introduction HOG Method Application Conclusion
Results
f (x) > 0 f (x) > 0.5
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Introduction HOG Method Application Conclusion
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.
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Parameters,
Windows extraction.
Perspectives
Improve 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 886–893, INRIA Rhone-Alpes, ZIRST-655, av. del’Europe, 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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