Pedestrian Detection using Infrared Images and Histograms of Oriented Gradients F. Suard1 , A. Rakotomamonjy1 , A. Bensrhair1 , A. Broggi2
[email protected] 1
Laboratoire d’Informatique, Traitement de l’Information, Syst`emes. INSA de Rouen, Rouen, France 2 Dipartimento di Ingegneria dell’Informazione, Universit` a 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. F. Suard
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HOG Method
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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 Method
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HOG computation steps Original Image :
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HOG Method
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HOG computation steps Gradient Orientation and Norm:
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HOG Method
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HOG computation steps Cell Splitting:
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HOG Method
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HOG computation steps Histogramm normalization, block 1:
Final descriptor: [0.01 0.5 0 0.8] F. Suard
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HOG Method
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HOG computation steps Histogramm normalization, block 2:
Final descriptor: [0.01 0.5 0 0.8 0.2 0 0.9 0] F. Suard
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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] F. Suard
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Introduction
HOG Method
Application
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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 Method
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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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HOG Method
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Linear SVM Classifier Data X ∈ Rn Label y ∈ {−1, 1} Decision function f (x) =
Pm
k=1 αk
· yk · hxk , xi + b
Class of X = sign of f (x)
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HOG Method
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Single Frame Classification ROC Curve for single frame classification (test dataset: 4400 examples) when size of learning dataset varies :
Confusion matrix (1000) True P N P 2096 54 Prediction 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 classification (test dataset: 4400 examples) when size of learning dataset varies :
Confusion matrix (1000) True P N P 2096 54 Prediction N 71 2079 detection 0.9749 accuracy 0.9709 precision 0.9672
For 90 % of good recognition : 1 false-positive for 330 windows examined F. Suard
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Introduction
HOG Method
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Misclassification
Examples of bad classification :
misclassification
misclassification
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false-positive
false-positive
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HOG Method
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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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HOG Method
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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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HOG Method
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Windows extraction combination and windows extraction (> 1000):
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Windows extraction Classification:
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HOG Method
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Results Windows which prediction are over threshold :
f (x) > 0
f (x) > 0.5 F. Suard
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HOG Method
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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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HOG Method
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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. Perspectives Improve performance, reduce computation time, work with sequences. F. Suard
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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 Computer Vision and Pattern Recognition, volume 2, pages 886–893, INRIA Rhone-Alpes, ZIRST-655, av. de l’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 pedestrian detection. 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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