probabilistic framework for multi-target tracking using...
Post on 28-Sep-2020
6 Views
Preview:
TRANSCRIPT
Probabilistic framework for multi-target tracking
using multi-camera: applied to fall detection
Master thesis presentation
Presented by: Victoria Rudakova
Supervisor: Prof. Faouzi Alaya Cheikh
Color in Informatics and MEdia Technology
Gjøvik University College
June 4 2010
Introduction Previous work Proposed solution Conclusions
Outline
1 Introduction
2 Previous work
3 Proposed solution
4 Conclusions
Introduction Previous work Proposed solution Conclusions
Outline
1 Introduction
2 Previous work
3 Proposed solution
4 Conclusions
Introduction Previous work Proposed solution Conclusions
Motivation
The population of elderly grows → need in new technologiesto insure their safety
Falling down is a greatest danger for elderly
The main question: how to detect a fall or maybe prevent it?
Classical methods: using wearable sensors
But: sometimes not very effective
Possible solution?
Video-based approach.
Introduction Previous work Proposed solution Conclusions
Motivation
The population of elderly grows → need in new technologiesto insure their safety
Falling down is a greatest danger for elderly
The main question: how to detect a fall or maybe prevent it?
Classical methods: using wearable sensors
But: sometimes not very effective
Possible solution?
Video-based approach.
Introduction Previous work Proposed solution Conclusions
Motivation
The population of elderly grows → need in new technologiesto insure their safety
Falling down is a greatest danger for elderly
The main question: how to detect a fall or maybe prevent it?
Classical methods: using wearable sensors
But: sometimes not very effective
Possible solution?
Video-based approach.
Introduction Previous work Proposed solution Conclusions
Motivation
The population of elderly grows → need in new technologiesto insure their safety
Falling down is a greatest danger for elderly
The main question: how to detect a fall or maybe prevent it?
Classical methods: using wearable sensors
But: sometimes not very effective
Possible solution?
Video-based approach.
Introduction Previous work Proposed solution Conclusions
Problem statement
The main objective
Build a robust multi-camera multi-target tracking system as abasis for high-level analysis - fall detection
Requirements
tracking and identification of multiple targets
handling mutual occlusions
cope with background clutter, illumination changes, shadows,etc.
Introduction Previous work Proposed solution Conclusions
Problem statement
The main objective
Build a robust multi-camera multi-target tracking system as abasis for high-level analysis - fall detection
Requirements
tracking and identification of multiple targets
handling mutual occlusions
cope with background clutter, illumination changes, shadows,etc.
Introduction Previous work Proposed solution Conclusions
General block-scheme of the system
Introduction Previous work Proposed solution Conclusions
?Research questions?
Multi-target tracking
target detection
target tracking
resolving occlusions
background clutter, illumination changes, shadows, etc.
Multi-camera tracking
avoid camera calibration
multi-view data fusion
Activity recognition
distinguish falls from other everyday activities
Introduction Previous work Proposed solution Conclusions
?Research questions?
Multi-target tracking
target detection
target tracking
resolving occlusions
background clutter, illumination changes, shadows, etc.
Multi-camera tracking
avoid camera calibration
multi-view data fusion
Activity recognition
distinguish falls from other everyday activities
Introduction Previous work Proposed solution Conclusions
?Research questions?
Multi-target tracking
target detection
target tracking
resolving occlusions
background clutter, illumination changes, shadows, etc.
Multi-camera tracking
avoid camera calibration
multi-view data fusion
Activity recognition
distinguish falls from other everyday activities
Introduction Previous work Proposed solution Conclusions
Illustration of the problem
Introduction Previous work Proposed solution Conclusions
Outline
1 Introduction
2 Previous work
3 Proposed solution
4 Conclusions
Introduction Previous work Proposed solution Conclusions
At HIG
People detection and tracking
CAMSHIFT combined with optical flow using single camera
Single camera DOES NOT
cover all the monitored area
provide robust tracking for multi-targets (occlusions)
Introduction Previous work Proposed solution Conclusions
At HIG
People detection and tracking
CAMSHIFT combined with optical flow using single camera
Single camera DOES NOT
cover all the monitored area
provide robust tracking for multi-targets (occlusions)
Introduction Previous work Proposed solution Conclusions
Multi-view setup
Second camera
helps to resolve occlusions
extends the FOV
Introduction Previous work Proposed solution Conclusions
Data fusion
Multiple cameras
Build a correspondence between different views
Most popular methods
homography
epipolar geometry
Drawback
Requires camera calibration or other initial configuration
Conclusions
view correspondence must be based on some probability /confidence function
it constrains tracking algorithm to be probability-based also
Introduction Previous work Proposed solution Conclusions
Data fusion
Multiple cameras
Build a correspondence between different views
Most popular methods
homography
epipolar geometry
Drawback
Requires camera calibration or other initial configuration
Conclusions
view correspondence must be based on some probability /confidence function
it constrains tracking algorithm to be probability-based also
Introduction Previous work Proposed solution Conclusions
Data fusion
Multiple cameras
Build a correspondence between different views
Most popular methods
homography
epipolar geometry
Drawback
Requires camera calibration or other initial configuration
Conclusions
view correspondence must be based on some probability /confidence function
it constrains tracking algorithm to be probability-based also
Introduction Previous work Proposed solution Conclusions
Data fusion
Multiple cameras
Build a correspondence between different views
Most popular methods
homography
epipolar geometry
Drawback
Requires camera calibration or other initial configuration
Conclusions
view correspondence must be based on some probability /confidence function
it constrains tracking algorithm to be probability-based also
Introduction Previous work Proposed solution Conclusions
Outline
1 Introduction
2 Previous work
3 Proposed solution
4 Conclusions
Introduction Previous work Proposed solution Conclusions
System overview
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: notations
consider target evolution as STATE transition process thatcould be described by some model
a state:
xt ∈ Rnx , t ∈ N - the current time step
represented by a vector - coordinates, velocities, scale etc.
The objective
Evaluate current state of the target given observations (data)
An observation: zt ∈ Rnz , t ∈ N - the current time step
graphical modeling helps to represent a relationships betweenthese two variables
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: notations
consider target evolution as STATE transition process thatcould be described by some model
a state:
xt ∈ Rnx , t ∈ N - the current time step
represented by a vector - coordinates, velocities, scale etc.
The objective
Evaluate current state of the target given observations (data)
An observation: zt ∈ Rnz , t ∈ N - the current time step
graphical modeling helps to represent a relationships betweenthese two variables
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: notations
consider target evolution as STATE transition process thatcould be described by some model
a state:
xt ∈ Rnx , t ∈ N - the current time step
represented by a vector - coordinates, velocities, scale etc.
The objective
Evaluate current state of the target given observations (data)
An observation: zt ∈ Rnz , t ∈ N - the current time step
graphical modeling helps to represent a relationships betweenthese two variables
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: notations
consider target evolution as STATE transition process thatcould be described by some model
a state:
xt ∈ Rnx , t ∈ N - the current time step
represented by a vector - coordinates, velocities, scale etc.
The objective
Evaluate current state of the target given observations (data)
An observation: zt ∈ Rnz , t ∈ N - the current time step
graphical modeling helps to represent a relationships betweenthese two variables
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: notations
consider target evolution as STATE transition process thatcould be described by some model
a state:
xt ∈ Rnx , t ∈ N - the current time step
represented by a vector - coordinates, velocities, scale etc.
The objective
Evaluate current state of the target given observations (data)
An observation: zt ∈ Rnz , t ∈ N - the current time step
graphical modeling helps to represent a relationships betweenthese two variables
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: graphical models
A relationship between an observation zt and hidden state xt :
HMM serves well when describing a sequential data:
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: posterior distribution
Evolution
system state dynamics
xt = ft(xt−1, vt−1) (1)
observation dynamics
zt = ht(xt , ut) (2)
Tracking problem in Bayesian context
recursively calculate a belief degree p(xt |z1:t)
prior p(x0|z0) ≡ p(x0) is given
Markov assumptions works a
aFirst order Markov chain: xt⊥z0:t−1|xt−1 and zt⊥z0:t−1|xt
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: posterior distribution
Evolution
system state dynamics
xt = ft(xt−1, vt−1) (1)
observation dynamics
zt = ht(xt , ut) (2)
Tracking problem in Bayesian context
recursively calculate a belief degree p(xt |z1:t)
prior p(x0|z0) ≡ p(x0) is given
Markov assumptions works a
aFirst order Markov chain: xt⊥z0:t−1|xt−1 and zt⊥z0:t−1|xt
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: posterior distribution (cont.)
Posterior distribution inducing
1 prediction:
p(xt |z1:t−1) =
∫p(xt |xt−1)p(xt−1|z1:t−1)dxt−1 (3)
2 updation: use zt to update through Bayes’ rule
p(xt |z1:t) =p(zt |xt)p(xt |z1:t−1)
αt
(4)
How to use it? What to know?
motion model p(xt |xt−1) - described by 1
perceptual model p(zt |xt) - described by 2
start from: p(x0|z0) =p(z0|x0)
p(z0)p(x0)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: posterior distribution (cont.)
Posterior distribution inducing
1 prediction:
p(xt |z1:t−1) =
∫p(xt |xt−1)p(xt−1|z1:t−1)dxt−1 (3)
2 updation: use zt to update through Bayes’ rule
p(xt |z1:t) =p(zt |xt)p(xt |z1:t−1)
αt
(4)
How to use it? What to know?
motion model p(xt |xt−1) - described by 1
perceptual model p(zt |xt) - described by 2
start from: p(x0|z0) =p(z0|x0)
p(z0)p(x0)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: posterior distribution (cont.)
Posterior distribution inducing
1 prediction:
p(xt |z1:t−1) =
∫p(xt |xt−1)p(xt−1|z1:t−1)dxt−1 (3)
2 updation: use zt to update through Bayes’ rule
p(xt |z1:t) =p(zt |xt)p(xt |z1:t−1)
αt
(4)
How to use it? What to know?
motion model p(xt |xt−1) - described by 1
perceptual model p(zt |xt) - described by 2
start from: p(x0|z0) =p(z0|x0)
p(z0)p(x0)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: posterior distribution (cont.)
Posterior distribution inducing
1 prediction:
p(xt |z1:t−1) =
∫p(xt |xt−1)p(xt−1|z1:t−1)dxt−1 (3)
2 updation: use zt to update through Bayes’ rule
p(xt |z1:t) =p(zt |xt)p(xt |z1:t−1)
αt
(4)
How to use it? What to know?
motion model p(xt |xt−1) - described by 1
perceptual model p(zt |xt) - described by 2
start from: p(x0|z0) =p(z0|x0)
p(z0)p(x0)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: one dimentional illustration
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model:
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: two consecutive frames t − 1 and t
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: two cameras A and B
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: two layers - hidden (circles) and obervable(rectangles)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: state dynamics p(xt |xt−1)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: local observation likelihood p(zt |xt)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: ’interaction’
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: camera ’collaboration’
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking (cont.)
Generic statistical framework for one targer
p(xA,i0:t |z
A,i1:t , z
A,J1:t1:t , z
B,i1:t ) = kt p(zA,i
t |xA,it ) p(xA,i
t |xA,i0:t−1)
× p(zA,Jt
t |xA,it , z
A,it ) p(zB,i
t |xA,it )
× p(xA,i0:t−1|z
A,i1:t−1, z
A,J1:t−1
1:t−1 , zB,i1:t−1),
(5)
where
p(zA,it |x
A,it ) - local observation likelihood
p(xA,it |x
A,i0:t−1) - state dynamics
p(zA,Jt
t |xA,it , z
A,it ) - target interaction function
p(zB,it |x
A,it ) - camera collaboration function
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking (cont.)
Generic statistical framework for one targer
p(xA,i0:t |z
A,i1:t , z
A,J1:t1:t , z
B,i1:t ) = kt p(zA,i
t |xA,it ) p(xA,i
t |xA,i0:t−1)
× p(zA,Jt
t |xA,it , z
A,it ) p(zB,i
t |xA,it )
× p(xA,i0:t−1|z
A,i1:t−1, z
A,J1:t−1
1:t−1 , zB,i1:t−1),
(5)
where
p(zA,it |x
A,it ) - local observation likelihood
p(xA,it |x
A,i0:t−1) - state dynamics
p(zA,Jt
t |xA,it , z
A,it ) - target interaction function
p(zB,it |x
A,it ) - camera collaboration function
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking (cont.)
Generic statistical framework for one targer
p(xA,i0:t |z
A,i1:t , z
A,J1:t1:t , z
B,i1:t ) = kt p(zA,i
t |xA,it ) p(xA,i
t |xA,i0:t−1)
× p(zA,Jt
t |xA,it , z
A,it ) p(zB,i
t |xA,it )
× p(xA,i0:t−1|z
A,i1:t−1, z
A,J1:t−1
1:t−1 , zB,i1:t−1),
(5)
where
p(zA,it |x
A,it ) - local observation likelihood
p(xA,it |x
A,i0:t−1) - state dynamics
p(zA,Jt
t |xA,it , z
A,it ) - target interaction function
p(zB,it |x
A,it ) - camera collaboration function
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking (cont.)
Generic statistical framework for one targer
p(xA,i0:t |z
A,i1:t , z
A,J1:t1:t , z
B,i1:t ) = kt p(zA,i
t |xA,it ) p(xA,i
t |xA,i0:t−1)
× p(zA,Jt
t |xA,it , z
A,it ) p(zB,i
t |xA,it )
× p(xA,i0:t−1|z
A,i1:t−1, z
A,J1:t−1
1:t−1 , zB,i1:t−1),
(5)
where
p(zA,it |x
A,it ) - local observation likelihood
p(xA,it |x
A,i0:t−1) - state dynamics
p(zA,Jt
t |xA,it , z
A,it ) - target interaction function
p(zB,it |x
A,it ) - camera collaboration function
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking (cont.)
Generic statistical framework for one targer
p(xA,i0:t |z
A,i1:t , z
A,J1:t1:t , z
B,i1:t ) = kt p(zA,i
t |xA,it ) p(xA,i
t |xA,i0:t−1)
× p(zA,Jt
t |xA,it , z
A,it ) p(zB,i
t |xA,it )
× p(xA,i0:t−1|z
A,i1:t−1, z
A,J1:t−1
1:t−1 , zB,i1:t−1),
(5)
where
p(zA,it |x
A,it ) - local observation likelihood
p(xA,it |x
A,i0:t−1) - state dynamics
p(zA,Jt
t |xA,it , z
A,it ) - target interaction function
p(zB,it |x
A,it ) - camera collaboration function
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation
SMCM also are known as particle filter, condensation,bootstrap filter
Main idea
represent a posterior as a sample set with appropriate weights
p(xt |z0:t) ≈ x(i)t , ω
(i)t
Ns
i=1,
where x(i)t - one particle and ω
(i)t - its associated weight
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation
SMCM also are known as particle filter, condensation,bootstrap filter
Main idea
represent a posterior as a sample set with appropriate weights
p(xt |z0:t) ≈ x(i)t , ω
(i)t
Ns
i=1,
where x(i)t - one particle and ω
(i)t - its associated weight
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation(cont.)
Belief propagation
1 predict
p(xt |z0:t−1) ≈Ns∑i=1
p(xt |x(i)t−1)ω
(i)t−1
2 update
p(xt |z0:t) ≈Ns∑i=1
ω(i)t δ(xt − x
(i)t ),
where ω(i)t ∝
p(zt |x(i)t )p(x
(i)t |x
(i)t−1
)
q(x(i)t |x
(i)t−1,z0:t)
ω(i)t−1.
What to know?
dynamics model p(xt |x(i)t−1)
likelihood model p(zt |x(i)t )
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation(cont.)
Belief propagation
1 predict
p(xt |z0:t−1) ≈Ns∑i=1
p(xt |x(i)t−1)ω
(i)t−1
2 update
p(xt |z0:t) ≈Ns∑i=1
ω(i)t δ(xt − x
(i)t ),
where ω(i)t ∝
p(zt |x(i)t )p(x
(i)t |x
(i)t−1
)
q(x(i)t |x
(i)t−1,z0:t)
ω(i)t−1.
What to know?
dynamics model p(xt |x(i)t−1)
likelihood model p(zt |x(i)t )
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation(cont.)
Belief propagation
1 predict
p(xt |z0:t−1) ≈Ns∑i=1
p(xt |x(i)t−1)ω
(i)t−1
2 update
p(xt |z0:t) ≈Ns∑i=1
ω(i)t δ(xt − x
(i)t ),
where ω(i)t ∝
p(zt |x(i)t )p(x
(i)t |x
(i)t−1
)
q(x(i)t |x
(i)t−1,z0:t)
ω(i)t−1.
What to know?
dynamics model p(xt |x(i)t−1)
likelihood model p(zt |x(i)t )
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation(cont.)
Belief propagation
1 predict
p(xt |z0:t−1) ≈Ns∑i=1
p(xt |x(i)t−1)ω
(i)t−1
2 update
p(xt |z0:t) ≈Ns∑i=1
ω(i)t δ(xt − x
(i)t ),
where ω(i)t ∝
p(zt |x(i)t )p(x
(i)t |x
(i)t−1
)
q(x(i)t |x
(i)t−1,z0:t)
ω(i)t−1.
What to know?
dynamics model p(xt |x(i)t−1)
likelihood model p(zt |x(i)t )
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation: example
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation: particle filter demo
One particle is represented as an ellipse
particle filtering
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation: resampling
Degeneracy phenomenon
definition: all, but one particle have close to zero weights
→ most of computations is wasted on those particles withnegligible weights
Solution
Use resampling technique!
ignore particles with very low weights
concentrate attention on more promising particles
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation: resampling
Degeneracy phenomenon
definition: all, but one particle have close to zero weights
→ most of computations is wasted on those particles withnegligible weights
Solution
Use resampling technique!
ignore particles with very low weights
concentrate attention on more promising particles
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation: SIR scheme
x(i)t−1,N
−1Ni=1
approximates p(xt−1|z0:t−2)
update x(i)t−1, ω
(i)t−1
Ni=1 to
represent p(xt−1|z0:t−1)
resample to make
x(i)t−1,N
−1Ni=1
propagate to x(i)t ,N
−1Ni=1
to represent p(xt |y0:t−1)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation: resampling demos
One particle = an ellipse (centers are displayed)
no resampl. high-thresh. resampl.
Introduction Previous work Proposed solution Conclusions
Multi-target trackingTarget representation
5-dimentional parametric ellipse model
xt = (cxt , cyt , at , bt , ρt),
(cx , xy) - coordinates of the ellipse center(a, b) - major and minor axisesρ - orientation angle
Introduction Previous work Proposed solution Conclusions
Multi-target trackingModeling of densities
Local observation model p(zt |xt)
Single cue: color histogram model
State dynamics model p(xt |xt−1)
Motion-based proposal (Lucas-Kanade optical flow algorithm):
motion vector: ∆Vt = (cxt − cxt−1, cyt − cyt−1, 0, 0, 0)
sampling scheme: xt = xt−1 +∆Vt + ωt ,
where ωt - Gaussian noise
Introduction Previous work Proposed solution Conclusions
Multi-target trackingModeling of densities
Local observation model p(zt |xt)
Single cue: color histogram model
State dynamics model p(xt |xt−1)
Motion-based proposal (Lucas-Kanade optical flow algorithm):
motion vector: ∆Vt = (cxt − cxt−1, cyt − cyt−1, 0, 0, 0)
sampling scheme: xt = xt−1 +∆Vt + ωt ,
where ωt - Gaussian noise
Introduction Previous work Proposed solution Conclusions
Multi-target trackingState estimate
Given: cloud of particles x(i)t , ω
(i)t
Ns
i=1
Want to know: what will be the current state estimate?(Where our target is located?)
Solution
Weighted sum of particles!
mean shape
Introduction Previous work Proposed solution Conclusions
Multi-target trackingState estimate
Given: cloud of particles x(i)t , ω
(i)t
Ns
i=1
Want to know: what will be the current state estimate?(Where our target is located?)
Solution
Weighted sum of particles!
mean shape
Introduction Previous work Proposed solution Conclusions
Multi-target trackingInteraction model
when targets are occluding each other, can’t rely onmotion-based propagation anymore
use random-based prediction
inertia information could be of use for further data association
xt = xt−1 +∆Vt +Ω−t xt = Axt−1 +Ω+t
Introduction Previous work Proposed solution Conclusions
Multi-target trackingInteraction model: demo
random-based.
Introduction Previous work Proposed solution Conclusions
Multi-camera trackingMulti-camera data fusion: a problem illustration
A problem
How to associate targets in different views so that they have thesame identities?No calibration information is given!
Rely on appearance?
Introduction Previous work Proposed solution Conclusions
Multi-camera trackingMulti-camera data fusion: a problem illustration
A problem
How to associate targets in different views so that they have thesame identities?No calibration information is given!
Rely on appearance?
Introduction Previous work Proposed solution Conclusions
Multi-camera trackingMulti-camera data fusion: a problem illustration
A problem
How to associate targets in different views so that they have thesame identities?No calibration information is given!
Rely on appearance?
Introduction Previous work Proposed solution Conclusions
Multi-camera trackingMulti-camera data fusion: Gale-Shapley 1962 algortihm
General idea
given preference list for each target
helps to find a stable matching
Modifications
each preference has a probability (likelihood)
the preference list must be built beforehand
Drawbacks
2 cameras only
equal number of targets in both camera views
proposers optimality and acceptors pessimality
Introduction Previous work Proposed solution Conclusions
Multi-camera trackingMulti-camera data fusion: Gale-Shapley 1962 algortihm
General idea
given preference list for each target
helps to find a stable matching
Modifications
each preference has a probability (likelihood)
the preference list must be built beforehand
Drawbacks
2 cameras only
equal number of targets in both camera views
proposers optimality and acceptors pessimality
Introduction Previous work Proposed solution Conclusions
Multi-camera trackingMulti-camera data fusion: Gale-Shapley 1962 algortihm
General idea
given preference list for each target
helps to find a stable matching
Modifications
each preference has a probability (likelihood)
the preference list must be built beforehand
Drawbacks
2 cameras only
equal number of targets in both camera views
proposers optimality and acceptors pessimality
Introduction Previous work Proposed solution Conclusions
Fall detectionFeature extraction
Extracted features will be used later for activity recognition
Silhouette-based features
basic: aspect ratio, height of the center of mass, orientation,major and minor axises, height of the bounding box
advanced: edge histogram
Motion-based features
motion direction, speed value, motion change gradient, etc.
Output
Feature vector
Introduction Previous work Proposed solution Conclusions
Fall detectionFeature extraction
Extracted features will be used later for activity recognition
Silhouette-based features
basic: aspect ratio, height of the center of mass, orientation,major and minor axises, height of the bounding box
advanced: edge histogram
Motion-based features
motion direction, speed value, motion change gradient, etc.
Output
Feature vector
Introduction Previous work Proposed solution Conclusions
Fall detectionFeature extraction
Extracted features will be used later for activity recognition
Silhouette-based features
basic: aspect ratio, height of the center of mass, orientation,major and minor axises, height of the bounding box
advanced: edge histogram
Motion-based features
motion direction, speed value, motion change gradient, etc.
Output
Feature vector
Introduction Previous work Proposed solution Conclusions
Fall detectionClassification
The algorithm
Support Vector Machine (SVM)
helps to classify data into classes
2 classes: ’fall’ and ’no fall’
supervised learning method → needs some training
OpenCV implementation is available
Introduction Previous work Proposed solution Conclusions
Fall detectionClassification
The algorithm
Support Vector Machine (SVM)
helps to classify data into classes
2 classes: ’fall’ and ’no fall’
supervised learning method → needs some training
OpenCV implementation is available
Introduction Previous work Proposed solution Conclusions
Fall detectionClassification
The algorithm
Support Vector Machine (SVM)
helps to classify data into classes
2 classes: ’fall’ and ’no fall’
supervised learning method → needs some training
OpenCV implementation is available
Introduction Previous work Proposed solution Conclusions
Fall detectionClassification
The algorithm
Support Vector Machine (SVM)
helps to classify data into classes
2 classes: ’fall’ and ’no fall’
supervised learning method → needs some training
OpenCV implementation is available
Introduction Previous work Proposed solution Conclusions
Fall detectionClassification
The algorithm
Support Vector Machine (SVM)
helps to classify data into classes
2 classes: ’fall’ and ’no fall’
supervised learning method → needs some training
OpenCV implementation is available
Introduction Previous work Proposed solution Conclusions
Outline
1 Introduction
2 Previous work
3 Proposed solution
4 Conclusions
Introduction Previous work Proposed solution Conclusions
Conclusions
Multi-target tracking
Status: Bayesian framework was implemented and adapted
Analysis: tests for two people only
Extensions: use additional cues for robustness (e.g.PCA-based appearance model)
Multi-camera data fusion
Status: Gale-Shapley algorithm was implemented
Analysis: simulation of a system with 2 cameras
Extensions: extend from 2 to more cameras
Fall detection
Status: the procedure is described theoretically
Extensions: embed into the system and apply on databases fortracking and fall detection
Introduction Previous work Proposed solution Conclusions
Conclusions
Multi-target tracking
Status: Bayesian framework was implemented and adapted
Analysis: tests for two people only
Extensions: use additional cues for robustness (e.g.PCA-based appearance model)
Multi-camera data fusion
Status: Gale-Shapley algorithm was implemented
Analysis: simulation of a system with 2 cameras
Extensions: extend from 2 to more cameras
Fall detection
Status: the procedure is described theoretically
Extensions: embed into the system and apply on databases fortracking and fall detection
Introduction Previous work Proposed solution Conclusions
Conclusions
Multi-target tracking
Status: Bayesian framework was implemented and adapted
Analysis: tests for two people only
Extensions: use additional cues for robustness (e.g.PCA-based appearance model)
Multi-camera data fusion
Status: Gale-Shapley algorithm was implemented
Analysis: simulation of a system with 2 cameras
Extensions: extend from 2 to more cameras
Fall detection
Status: the procedure is described theoretically
Extensions: embed into the system and apply on databases fortracking and fall detection
top related