![Page 1: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/1.jpg)
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
![Page 2: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/2.jpg)
Introduction Previous work Proposed solution Conclusions
Outline
1 Introduction
2 Previous work
3 Proposed solution
4 Conclusions
![Page 3: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/3.jpg)
Introduction Previous work Proposed solution Conclusions
Outline
1 Introduction
2 Previous work
3 Proposed solution
4 Conclusions
![Page 4: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/4.jpg)
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.
![Page 5: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/5.jpg)
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.
![Page 6: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/6.jpg)
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.
![Page 7: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/7.jpg)
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.
![Page 8: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/8.jpg)
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.
![Page 9: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/9.jpg)
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.
![Page 10: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/10.jpg)
Introduction Previous work Proposed solution Conclusions
General block-scheme of the system
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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
![Page 12: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/12.jpg)
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
![Page 13: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/13.jpg)
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
![Page 14: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/14.jpg)
Introduction Previous work Proposed solution Conclusions
Illustration of the problem
![Page 15: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/15.jpg)
Introduction Previous work Proposed solution Conclusions
Outline
1 Introduction
2 Previous work
3 Proposed solution
4 Conclusions
![Page 16: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/16.jpg)
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)
![Page 17: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/17.jpg)
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)
![Page 18: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/18.jpg)
Introduction Previous work Proposed solution Conclusions
Multi-view setup
Second camera
helps to resolve occlusions
extends the FOV
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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
![Page 20: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/20.jpg)
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
![Page 21: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/21.jpg)
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
![Page 22: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/22.jpg)
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
![Page 23: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/23.jpg)
Introduction Previous work Proposed solution Conclusions
Outline
1 Introduction
2 Previous work
3 Proposed solution
4 Conclusions
![Page 24: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/24.jpg)
Introduction Previous work Proposed solution Conclusions
System overview
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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
![Page 26: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/26.jpg)
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
![Page 27: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/27.jpg)
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
![Page 28: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/28.jpg)
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
![Page 29: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/29.jpg)
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
![Page 30: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/30.jpg)
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:
![Page 31: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/31.jpg)
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
![Page 32: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/32.jpg)
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
![Page 33: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/33.jpg)
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)
![Page 34: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/34.jpg)
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)
![Page 35: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/35.jpg)
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)
![Page 36: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/36.jpg)
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)
![Page 37: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/37.jpg)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian sequential estimation: one dimentional illustration
![Page 38: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/38.jpg)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model:
![Page 39: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/39.jpg)
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
![Page 40: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/40.jpg)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: two cameras A and B
![Page 41: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/41.jpg)
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)
![Page 42: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/42.jpg)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: state dynamics p(xt |xt−1)
![Page 43: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/43.jpg)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: local observation likelihood p(zt |xt)
![Page 44: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/44.jpg)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: ’interaction’
![Page 45: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/45.jpg)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingBayesian framework for multi-target multi-camera tracking
Graphical model: camera ’collaboration’
![Page 46: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/46.jpg)
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
![Page 47: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/47.jpg)
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
![Page 48: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/48.jpg)
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
![Page 49: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/49.jpg)
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
![Page 50: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/50.jpg)
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
![Page 51: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/51.jpg)
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
![Page 52: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/52.jpg)
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
![Page 53: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/53.jpg)
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 )
![Page 54: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/54.jpg)
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 )
![Page 55: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/55.jpg)
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 )
![Page 56: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/56.jpg)
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 )
![Page 57: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/57.jpg)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation: example
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Introduction Previous work Proposed solution Conclusions
Multi-target trackingSequential Monte-Carlo implementation: particle filter demo
One particle is represented as an ellipse
particle filtering
![Page 59: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/59.jpg)
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
![Page 60: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/60.jpg)
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
![Page 61: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/61.jpg)
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)
![Page 62: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/62.jpg)
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.
![Page 63: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/63.jpg)
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
![Page 64: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/64.jpg)
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
![Page 65: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/65.jpg)
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
![Page 66: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/66.jpg)
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
![Page 67: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/67.jpg)
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
![Page 68: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/68.jpg)
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
![Page 69: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/69.jpg)
Introduction Previous work Proposed solution Conclusions
Multi-target trackingInteraction model: demo
random-based.
![Page 70: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/70.jpg)
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?
![Page 71: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/71.jpg)
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?
![Page 72: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/72.jpg)
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?
![Page 73: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/73.jpg)
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
![Page 74: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/74.jpg)
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
![Page 75: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/75.jpg)
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
![Page 76: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/76.jpg)
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
![Page 77: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/77.jpg)
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
![Page 78: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/78.jpg)
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
![Page 79: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/79.jpg)
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
![Page 80: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/80.jpg)
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
![Page 81: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/81.jpg)
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
![Page 82: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/82.jpg)
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
![Page 83: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/83.jpg)
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
![Page 84: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/84.jpg)
Introduction Previous work Proposed solution Conclusions
Outline
1 Introduction
2 Previous work
3 Proposed solution
4 Conclusions
![Page 85: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/85.jpg)
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
![Page 86: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/86.jpg)
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
![Page 87: Probabilistic framework for multi-target tracking using ...english.hig.no/content/download/28555/327673/file/... · Master thesis presentation Presented by: Victoria Rudakova Supervisor:](https://reader034.vdocuments.mx/reader034/viewer/2022052003/60169c1d0b8bf1044a33db12/html5/thumbnails/87.jpg)
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