application of bayesian network in human motion analysis
DESCRIPTION
Application of Bayesian Network in Human Motion Analysis. Ping Liu & Shizhong Han. What is Bayesian Network?. A Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). - PowerPoint PPT PresentationTRANSCRIPT
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Application of Bayesian Network in Human Motion Analysis
Ping Liu & Shizhong Han
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What is Bayesian Network?
A Bayesian network is a probabilistic graphical model that represents a set of random variables
and their conditional dependencies via a directed acyclic graph (DAG). It is a combination of Graph algorithm and Probabilistic theory
In Bayesian Network The node—random variables The edge—conditional dependencies The directed edge & undirected edge
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A sample of Bayesian Network
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A sample of Bayesian Network
Three nodes in this example: sprinkler, raining and grass wet. From the edges, we can conclude:
--Rain can lead to grass wet, and also open the sprinkler
--Open the sprinkler can lead to grass wet So The joint probability function is:
P(G,S,R) = P(G | S,R)P(S | R)P(R)
G = Grass wet, S = Sprinkler, and R = Rain.
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A sample of Bayesian Network
Here is a question based on the sample:
“What is the probability that it is raining, given the grass is wet?” We can solve this problem by using the conditional probability formula:
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A sample of Bayesian Network
What does this mean? How should we use the conclusion?
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How to apply BN into human motion analysis?
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How to apply BN into human motion analysis?-- a more complex example
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How to apply BN into human motion analysis?-- a more complex example
H5: Vertical pose of the outermost leg H6: Horizontal pose of the outermost
leg V8: Vertical position of maximum
curvature point of lowerbody convex hull
V9: Lower-body ellipse aspect ratio of major to minor axes
V10: Lower-body ellipse rotation on image plane
V11: Horizontal position of maximum curvature point of lowerbody convex hull
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How to apply BN into human motion analysis?-- a more complex example
How we get the dependence probability between each node? How we get the prior information of corresponding node?
The answer is :Use the information from the “training” data set.From the training data set, we will get the information about the prior
probability of node, the conditional dependence probability between nodes.
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How to apply BN into human motion analysis?--Estimation & Inference
We can predict the action based on the observation-- If Jack holds a baseball bat in his hand, is he playing base ball or
swimming?-- If Jack nods his head, does he mean ”yes” or “no”? We can estimate the “observation” based on the action --If Jack is playing base ball, it is probable that he holds a baseball bat in
his hand-- If Jack agrees with my opinion, it is not likely that he shakes his head
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Arm Pose Estimation H3: Vertical pos of the
outermost arm H4: Horizontal pose of
the outermost arm V4: Vertical position of
maximum curvature point of upper-body convex hull.
V5:Torso ellipse aspect ratio of major to minor axes
V6: Torso ellipse rotation on image plan
V7: Horizontal position of maximum curvature point of upper-body convex hull.
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Observation for the arm pose estimation. Individual upper body is represented by an ellipse A and B in (a) and the corresponding convex hull in (b). The maximum curvature points C and D in (b) are detected as candidate hand positions
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Our goal is to estimate the posterior probability probability P(H3:4|V4:7):
P(H3:4|V4:7)=P(V4:7|H3:4)P(H3:4) The hidden node's states are defined
bellow: H3={'low', 'mid-low', 'mid-high', 'high'} H4={'withdrawn','intermediate','stretching'}
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Leg Pose Estimation H5:Vertical pos of the
outermost leg H6:Horizontal pose of the
outermost leg V8:Vertical position of
maximum curvature point of lower-body convex hull.
V9:Lower-body ellipse aspect ratio of major to minor axes
V10:Lower-body ellipse rotation on image plan
V11: Horizontal position of maximum curvature point of lower-body convex hull.
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Observation for the leg pose estimation. Individual lower body is represented by an ellipse A and B in (a) and the corresponding convex hull in (b). The maximum curvature points C and D in (b) are detected as candidate leg positions
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Our goal is to estimate the posterior probability P(H5:6|V8:11):
P(H5:6|V8:11)=P(V8:11|H5:6)P(H5:6) The hidden node's states are defined bellow: H5={'low', 'mid-low', 'high'} H6={'withdrawn','intermediate','stretching'}
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Bayesian network for pose estimation of an interacting person. The BN is composed of 6 hidden nodes H1:6 and 11 visible nodes V1:11 .
H1(the torso pose)= {‘front-view’, ‘left-view’, ‘right-view’, ‘rear-view’}. V3:the median width of the torso in an image
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Our goal is to estimate the posterior probabilities P(H1|H2:6,V3):
P(H1|H2:6,V3 )=P(H2:6 ,V3|H1)P(H1 )
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Reference
Recognition of two-person interactions using a hierarchical Bayesian network
--Park, S. and Aggarwal, JK http://en.wikipedia.org/wiki/Bayesian_network Graphical Models: Methods for Data Analysis and Mining
-- Borgelt, Christian; Kruse, Rudolf