human gesture recognition using kinect camera presented by carolina vettorazzo and diego santo orasa...
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1
Human Gesture Recognition Using Kinect Camera
Presented by Carolina Vettorazzo and Diego Santo
Orasa Patsadu, Chakarida Nukoolkit and Bunthit Watanapa
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IntroductionThis work proposes a comparison
of human gesture recognition using data mining classification methods
The gestures where chosen to be the knowledge base of a smart home system which monitors and detects the fall motion of the elderly or hospital patients.
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IntroductionHuman gesture
◦Hands, arms, and body◦Movements of the head, face, and
eyes
Performance of recognition methods◦Light conditions◦Shadows◦Camera angle◦Occlusion
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The Kinect
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The Kinect - depth imageA pattern of IR dots is projected
from the sensor
These dots are detected by the IR camera
The dots will change position based on how far the objects are from the source.
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The Kinect - depth image
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The Kinect - depth image
Shotton et al, CVPR(2011)
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The Kinect - Skeleton
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The Kinect - applicationsKinect Gesture Recognition REALT
IME
Kinect-based Hand Gesture Recognition
http://kinectpowerpoint.codeplex.com/
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The Kinect - applicationsRehabilitation.
Improvement of athletes performance.
Interactive surfaces.
3D modeling.
Augmented reality
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MethodologyData mining classification
◦It is the process of extracting valid, previously unseen or unknown, comprehensible information from large databases
◦Algorithms can involve artificial intelligence, machine learning, statistics, and database systems.
z-score normalization◦improve the accuracy and efficiency of
mining algorithms
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Classification MethodsIn this study, were selected four popular data
mining classification method were selected :◦ Back Propagation Neural Network (BPNN)◦ Support Vector Machine (SVM)◦ Decision Tree◦ Naїve Bayes
To identify three human gestures:◦ Stand ◦ Sit down◦ Lie down
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Classification MethodsProcess of Classification
Figure 1: Overview of the proposed system
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Classification MethodsProcess of Classification
◦1,200 input vectors for each of the three human gesture classes in input data
◦3,600 input vectors (x,y,z) for each distance setting as shown (Stand, Sit down, Lie Down).
◦7,200 input vectors in total for both camera distance settings (2m and 3m)
◦1,200 vectors for both camera distance settings (2m and 3m)
◦The output data contain 3,600 vectors in total
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Classification MethodsBackpropagation Neural
Network(BPNN)◦ BPNN is a multilayer feed forward neural
network, which uses backpropagation algorithm in its learning.
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Classification MethodsSupport Vector Machine (SVM)
◦ In machine learning, support vector machines
(SVMs, also support vector networks)
are supervised learning models with associated
learning algorithms that analyze data and recognize
patterns
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Classification MethodsDecision Tree (DT)
◦Decision Tree is used to classify data from class label
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Classification MethodsNaïve Bayes (NB)
◦Is a statistical classification which predicts class membership based on conditional probabilities.
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Human Gestures
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ResultsBPNN 100%SVM 99.75%DT 93.19%NB 81.94%
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Questions???