Continuation of the previous one..
Basically, my system is extracting the features to recognize gesture and
postures of the hand and this is a problem which comes in case of
occlusion. 
Omer 

>Tim Wescott wrote:
>> Jim Thomas wrote:
>>> Tim Wescott wrote:
>>>>
>>>> Jerry's suggestion of tracking the forearm position is probably based

>>>> on the notion that when you're waving your hand around (at least in 
>>>> the lab) your elbow isn't going to move as much as your hand, so your

>>>> hand will tend to move in an arc whose radius is is approximately 
>>>> equal to the length of your radius (I would have said 'ulna', but 
>>>> that wouldn't be as humerus).  
>>>
>>> This is why I come to comp.dsp almost every day.  Thanks!
>>>
>> For the down-to-earth practical info, or for the bad puns?
>
>The only bad puns are the ones you didn't think of first.
>
>Jerry
>-- 
>Engineering is the art of making what you want from things you can get.
>�����������������������������������������������������������������������
>

Hello,
I don't extract the forearm position as it is not always visible in the
images. I am extracting the hand, face with full sleeve shirt for
simplicity. Can I make a mathematical model with Extended Kalman Filter
with the position, velocity, acceleration etc or these features are not
enough? Thanks for the suggestions. I will read about the H-Infinity filter
today.  
Omer 

>Tim Wescott wrote:
>> Jim Thomas wrote:
>>> Tim Wescott wrote:
>>>>
>>>> Jerry's suggestion of tracking the forearm position is probably based

>>>> on the notion that when you're waving your hand around (at least in 
>>>> the lab) your elbow isn't going to move as much as your hand, so your

>>>> hand will tend to move in an arc whose radius is is approximately 
>>>> equal to the length of your radius (I would have said 'ulna', but 
>>>> that wouldn't be as humerus).  
>>>
>>> This is why I come to comp.dsp almost every day.  Thanks!
>>>
>> For the down-to-earth practical info, or for the bad puns?
>
>The only bad puns are the ones you didn't think of first.
>
>Jerry
>-- 
>Engineering is the art of making what you want from things you can get.
>�����������������������������������������������������������������������
>

Tim Wescott wrote:
> Jim Thomas wrote:
>> Tim Wescott wrote:
>>>
>>> Jerry's suggestion of tracking the forearm position is probably based 
>>> on the notion that when you're waving your hand around (at least in 
>>> the lab) your elbow isn't going to move as much as your hand, so your 
>>> hand will tend to move in an arc whose radius is is approximately 
>>> equal to the length of your radius (I would have said 'ulna', but 
>>> that wouldn't be as humerus).  
>>
>> This is why I come to comp.dsp almost every day.  Thanks!
>>
> For the down-to-earth practical info, or for the bad puns?

The only bad puns are the ones you didn't think of first.

Jerry
-- 
Engineering is the art of making what you want from things you can get.
�����������������������������������������������������������������������

Jim Thomas wrote:
> Tim Wescott wrote:
>>
>> Jerry's suggestion of tracking the forearm position is probably based 
>> on the notion that when you're waving your hand around (at least in 
>> the lab) your elbow isn't going to move as much as your hand, so your 
>> hand will tend to move in an arc whose radius is is approximately 
>> equal to the length of your radius (I would have said 'ulna', but that 
>> wouldn't be as humerus).  
> 
> This is why I come to comp.dsp almost every day.  Thanks!
> 
For the down-to-earth practical info, or for the bad puns?

-- 

Tim Wescott
Wescott Design Services
http://www.wescottdesign.com

Do you need to implement control loops in software?
"Applied Control Theory for Embedded Systems" gives you just what it says.
See details at http://www.wescottdesign.com/actfes/actfes.html

Tim Wescott wrote:
> 
> Jerry's suggestion of tracking the forearm position is probably based on 
> the notion that when you're waving your hand around (at least in the 
> lab) your elbow isn't going to move as much as your hand, so your hand 
> will tend to move in an arc whose radius is is approximately equal to 
> the length of your radius (I would have said 'ulna', but that wouldn't 
> be as humerus).  

This is why I come to comp.dsp almost every day.  Thanks!

-- 
Jim Thomas            Principal Applications Engineer  Bittware, Inc
jthomas@bittware.com  http://www.bittware.com    (603) 226-0404 x536
Dyslexics of the world, UNTIE!

omershad wrote:
(top posting fixed)
>> omershad wrote:
>>> Hello All, 
>>> I want to use the kalman filter for the tracking of the hand in case
> of
>>> occlusion. Hand can be moved freely in the environment and the
> occlusion
>>> can be with the other hand or with the face. In case of occlusion, I
> have
>>> to predict the hand or face from the previous motion. 
>>> For the simple case, I am drawing a rectangle on the corners of the
> hand
>>> and face and want to predict the rectangle points(four points) in case
> of
>>> occlusion. Is it possible to do that with Kalman filter? It is always
>>> prediction linearly and the results are not according to what I want
>>> because the motion can vary in any direction. I have tried that.
> However, I
>>> don't know exact process noise. For me the process noise can vary about
> 5
>>> pixels or so because in case of two consective frame, motion can not
> vary
>>> more than 5 pixels. 
>>> The second thing is that should I go for the Extended Kalman Filter?
> It
>>> works for non-linear ones. The thing which I have studied about EKF is
>>> about the functions in the transition matrixes? Can someone help me in
> this
>>> problem. If you want some more information or the problem is not
> clear,
>>> please ask. 
>> Have you considered using the position of the forearm as relevant 
>> information?
>>
>> Jerry
>> -- 
>> Engineering is the art of making what you want from things you can get.
>> ???????????????????????????????????????????????????????????????????????
>
> Hello, 
> I have got the position of the hand(x,y) and the velocity(vx,vy) which I
> can use for the prediction. Basically this is my state vector
> (position_x,position_y,velocity_x,velocity_y) and I want to find the
> predict the position in the next frame.
> Regards,
> Omer 
> 
Kalman filter performance can be very sensitive to the match between the 
model used and the actual system that's generating the signal.

Jerry's suggestion of tracking the forearm position is probably based on 
the notion that when you're waving your hand around (at least in the 
lab) your elbow isn't going to move as much as your hand, so your hand 
will tend to move in an arc whose radius is is approximately equal to 
the length of your radius (I would have said 'ulna', but that wouldn't 
be as humerus).  Modeling the hand-arm system will help you model the 
hand behavior more accurately.

Not only are you trying to model a nonlinear system (which would 
indicate that you should consider an EKF), but you really can't 
accurately model the whims of the human mind with a linear system 
excited by Gaussian noise.  So you may wish to use an H-infinity filter, 
or some 'extended' version if there is such a thing.  If this is more an 
academic project than a real-world one a particle filter may be 
interesting to try.  My understanding of them is that they really aren't 
ready for prime time yet, but that they are cool and have some distinct 
performance advantages.

My current favorite resource on Kalman filtering is Dan Simon's "Optimal 
State Estimation".  I can't claim to be an expert, but I certainly 
appreciate his approach.

-- 

Tim Wescott
Wescott Design Services
http://www.wescottdesign.com

Do you need to implement control loops in software?
"Applied Control Theory for Embedded Systems" gives you just what it says.
See details at http://www.wescottdesign.com/actfes/actfes.html

Hello, 
I have got the position of the hand(x,y) and the velocity(vx,vy) which I
can use for the prediction. Basically this is my state vector
(position_x,position_y,velocity_x,velocity_y) and I want to find the
predict the position in the next frame.
Regards,
Omer 

>omershad wrote:
>> Hello All, 
>> I want to use the kalman filter for the tracking of the hand in case
of
>> occlusion. Hand can be moved freely in the environment and the
occlusion
>> can be with the other hand or with the face. In case of occlusion, I
have
>> to predict the hand or face from the previous motion. 
>> For the simple case, I am drawing a rectangle on the corners of the
hand
>> and face and want to predict the rectangle points(four points) in case
of
>> occlusion. Is it possible to do that with Kalman filter? It is always
>> prediction linearly and the results are not according to what I want
>> because the motion can vary in any direction. I have tried that.
However, I
>> don't know exact process noise. For me the process noise can vary about
5
>> pixels or so because in case of two consective frame, motion can not
vary
>> more than 5 pixels. 
>> The second thing is that should I go for the Extended Kalman Filter?
It
>> works for non-linear ones. The thing which I have studied about EKF is
>> about the functions in the transition matrixes? Can someone help me in
this
>> problem. If you want some more information or the problem is not
clear,
>> please ask. 
>
>Have you considered using the position of the forearm as relevant 
>information?
>
>Jerry
>-- 
>Engineering is the art of making what you want from things you can get.
>???????????????????????????????????????????????????????????????????????
>

omershad wrote:
> Hello All, 
> I want to use the kalman filter for the tracking of the hand in case of
> occlusion. Hand can be moved freely in the environment and the occlusion
> can be with the other hand or with the face. In case of occlusion, I have
> to predict the hand or face from the previous motion. 
> For the simple case, I am drawing a rectangle on the corners of the hand
> and face and want to predict the rectangle points(four points) in case of
> occlusion. Is it possible to do that with Kalman filter? It is always
> prediction linearly and the results are not according to what I want
> because the motion can vary in any direction. I have tried that. However, I
> don't know exact process noise. For me the process noise can vary about 5
> pixels or so because in case of two consective frame, motion can not vary
> more than 5 pixels. 
> The second thing is that should I go for the Extended Kalman Filter? It
> works for non-linear ones. The thing which I have studied about EKF is
> about the functions in the transition matrixes? Can someone help me in this
> problem. If you want some more information or the problem is not clear,
> please ask. 

Have you considered using the position of the forearm as relevant 
information?

Jerry
-- 
Engineering is the art of making what you want from things you can get.
�����������������������������������������������������������������������

Hello All, 
I want to use the kalman filter for the tracking of the hand in case of
occlusion. Hand can be moved freely in the environment and the occlusion
can be with the other hand or with the face. In case of occlusion, I have
to predict the hand or face from the previous motion. 
For the simple case, I am drawing a rectangle on the corners of the hand
and face and want to predict the rectangle points(four points) in case of
occlusion. Is it possible to do that with Kalman filter? It is always
prediction linearly and the results are not according to what I want
because the motion can vary in any direction. I have tried that. However, I
don't know exact process noise. For me the process noise can vary about 5
pixels or so because in case of two consective frame, motion can not vary
more than 5 pixels. 
The second thing is that should I go for the Extended Kalman Filter? It
works for non-linear ones. The thing which I have studied about EKF is
about the functions in the transition matrixes? Can someone help me in this
problem. If you want some more information or the problem is not clear,
please ask. 
Omer