adaboost & genetic algorithms: application to pedestrian detection yotam abramson ecole des...
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AdaBoost & Genetic AdaBoost & Genetic algorithms: application to algorithms: application to
pedestrian detectionpedestrian detection
Yotam AbramsonYotam Abramson
Ecole des Mines de ParisEcole des Mines de Paris
9/12/059/12/05
Korea-France SafeMove Workshop
Korea-France SafeMove Workshop
Machine learning for visual object Machine learning for visual object detectiondetection
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ApplicationApplication
Pedestrian impact predictorPedestrian impact predictor
Calculates the probability of an impact between Calculates the probability of an impact between our car and a pedestrian.our car and a pedestrian.
If the probability is higher then a given If the probability is higher then a given threshold, an alert to the driver is issued or an threshold, an alert to the driver is issued or an action is taken (pedestrian airbag, braking…)action is taken (pedestrian airbag, braking…)
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Machine learning for visual object Machine learning for visual object detectiondetection
Learning algorithms for object-detection Learning algorithms for object-detection were shown to be better than any hand-were shown to be better than any hand-crafted ones.crafted ones.
Main works in the field:Main works in the field: Papageorgiou & Poggio – SVM,wavelets.Papageorgiou & Poggio – SVM,wavelets. Viola & Jones – AdaBoost and simple Viola & Jones – AdaBoost and simple
features.features.
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Machine learning - backgroundMachine learning - background
Support Vector Machine (SVM) – Vapnik Support Vector Machine (SVM) – Vapnik 19901990
Neural networkNeural network
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Machine learning Machine learning (Cont.)(Cont.)
AdaBoost (Freund & Schapire 1995):AdaBoost (Freund & Schapire 1995): A popular learning algorithm.A popular learning algorithm. Easy to understand.Easy to understand. Received a lot of attention in the machine Received a lot of attention in the machine
learning and statistics communities.learning and statistics communities.
The notion of The notion of boosting boosting (AdaBoost = (AdaBoost = adaptive boosting).adaptive boosting).
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AdaBoost at a GlanceAdaBoost at a Glance
Assume that we have a simple object Assume that we have a simple object classifier, that receives a rectangle in the classifier, that receives a rectangle in the image and decides if it’s the object.image and decides if it’s the object. For exampleFor example::
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AdaBoost at a Glance (Cont.)AdaBoost at a Glance (Cont.)
A classifier like the one shown is called a A classifier like the one shown is called a weak classifier.weak classifier. And indeed it is weak.. And indeed it is weak..
AdaBoost selects (=learns) a set of AdaBoost selects (=learns) a set of classifiers and builds a “voting system”.classifiers and builds a “voting system”.
Weak1 Weak2 Weak3 Weak4
Yes Yes No YesYes
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AdaBoost at a Glance (Cont.)AdaBoost at a Glance (Cont.)
Voting is not “democratic”… there is a Voting is not “democratic”… there is a weight for each weak-classifier.weight for each weak-classifier.
Weak1 Weak2 Weak3 Weak4
Yes Yes NO YesNo
0.2 0.7 0.20.2
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AdaBoost at a Glance (Cont.)AdaBoost at a Glance (Cont.)
The output of AdaBoost is a called a The output of AdaBoost is a called a strong classifier. strong classifier.
AdaBoost was used for face, cars and AdaBoost was used for face, cars and pedestrian detection by viola and Jones pedestrian detection by viola and Jones (2000).(2000).
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We have developed new We have developed new kinds of weak classifiers.kinds of weak classifiers.
Our features are different Our features are different because because they test they test individual pixelsindividual pixels..
They deal better with the They deal better with the variation in illumination.variation in illumination.
Weak classifiersWeak classifiers
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Illumination independent features Illumination independent features (cont.)(cont.)
Our features are highly Our features are highly efficient (3-4 image efficient (3-4 image access operations)access operations)
2 times faster than 2 times faster than Viola&JonesViola&Jones
20% of the memory20% of the memory
BetterBetter detection rates for detection rates for pedestrianspedestrians
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Learning Learning processprocessusingusinggeneticgeneticalgorithmalgorithm
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SevilleSeville
SEmi-SEmi-automatic automatic VIsuaL VIsuaL LearningLearning
(With Dr. Yoav (With Dr. Yoav Freund, Freund, Columbia Columbia University)University)
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SevilleSeville
We start by We start by collecting 10 collecting 10 negative and negative and positive positive examples. examples. We run the We run the learning, and learning, and classify. classify.
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SevilleSeville
We now have We now have 100 examples. 100 examples. We run We run learning, and learning, and the results the results improve.improve.
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SevilleSeville
We test another We test another sequence. We sequence. We collect in the collect in the same way same way more more examples. We examples. We re-run the re-run the learning and learning and continue.continue.
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SevilleSeville
We test another We test another sequence. We sequence. We collect in the collect in the same way more same way more examples. We examples. We re-run the re-run the learning and learning and continue.continue.
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SevilleSeville
Throughout the Throughout the phases, we phases, we use 2/3 of the use 2/3 of the set as training set as training set, and 1/3 as set, and 1/3 as validation set. validation set. We make We make AdaBoost AdaBoost rounds until rounds until the point of the point of overfitting.overfitting.
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European projectEuropean projectCAMELLIACAMELLIA
European unionEuropean union
Renault, Philips, Philips Renault, Philips, Philips semiconductor, Uni. semiconductor, Uni. Hannover, Uni. Las-palmasHannover, Uni. Las-palmas
“Smart camera”“Smart camera”
CAMELLIACAMELLIA
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CAMELLIA was used CAMELLIA was used also for other applicationsalso for other applications
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ConclusionsConclusions
We have presented a system for detection We have presented a system for detection of pedestrians.of pedestrians.
The system is based on AdaBoost and The system is based on AdaBoost and Genetic algorithms.Genetic algorithms.
The system was tested and gives good The system was tested and gives good results on real data.results on real data.