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Human Gesture Recognition Using Kinect Camera
Presented by Carolina Vettorazzo and Diego Santo
Orasa Patsadu, Chakarida Nukoolkit and Bunthit Watanapa
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.
IntroductionHuman gesture
◦Hands, arms, and body◦Movements of the head, face, and
eyes
Performance of recognition methods◦Light conditions◦Shadows◦Camera angle◦Occlusion
The Kinect
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.
The Kinect - depth image
The Kinect - depth image
Shotton et al, CVPR(2011)
The Kinect - Skeleton
The Kinect - applicationsKinect Gesture Recognition REALT
IME
Kinect-based Hand Gesture Recognition
http://kinectpowerpoint.codeplex.com/
The Kinect - applicationsRehabilitation.
Improvement of athletes performance.
Interactive surfaces.
3D modeling.
Augmented reality
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
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
Classification MethodsProcess of Classification
Figure 1: Overview of the proposed system
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
Classification MethodsBackpropagation Neural
Network(BPNN)◦ BPNN is a multilayer feed forward neural
network, which uses backpropagation algorithm in its learning.
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
Classification MethodsDecision Tree (DT)
◦Decision Tree is used to classify data from class label
Classification MethodsNaïve Bayes (NB)
◦Is a statistical classification which predicts class membership based on conditional probabilities.
Human Gestures
ResultsBPNN 100%SVM 99.75%DT 93.19%NB 81.94%
Questions???