thank you um that of this uh presentation is a state driven particle filter for
multi person tracking um these are so work with uh for that it let us
to and they're gonna look this
this is the other end of the of the presentation first uh define the introduction
to the problem of tracking
then i will uh
formulate a explain how the particle filtering general is formulated
uh you have touched and all that yeah
uh
afterwards
it's similar compute its own a computer
this is not this computer this is the laptop
oh
so it's uh
okay thank you
and
afterwards i will uh explain our proposal which is based on particle filtering and use
the state driven particle filtering and finally we present experimental results on the completion
well us most of you may know a multi person tracking have different applications for
instance or radiance driver assistance uh robot interaction with the media
um in sometimes uh the some pickups that some typical problems such as occlusions or
more than fifteen or lost targets detection is not uh addressed by the literature uh
people some things that present day um we propose something general or ready
basis in which these uh occlusions and problems doesn't happen uh so in this case
we have an example of target hijacking we have a target in the foreground weeks
recruits one in the background and high get the model
oh here we would have the problem of uh lost target detection uh and occluded
person by the bigram soul or by the scene
and um in general approaches and tracking dealing with tracking and not they do not
take these situations into account uh but typically um they use heuristics which are based
on a book um solutions for example detecting the targets overlapping or um stopping the
updating with the model updating when this happens getting the targets when detections are not
associated for a given set of frames et cetera uh some specific and papers which
uh really thinking talk on this problem by Q for example using um with the
common sff um so who proposed a post processing you know that to um you
know it to uh relate the relation the interactions between objects but this is made
as a post processing for the tracking for the for the acquisition and one hundred
depending on who proposed uh occlusion up that uh structures
a orange or object if is to formalize these problems uh in a in a
whole general framework you know what i what chuck hijacking model drifting decrease the false
positives the false negatives and also probably the identity switch yeah in the trucks
how do we make that well our contribution is and based on a graph uh
continue containing states this estates are formalising their in the nodes and uh the transitions
and between the nodes
are represented by the arcs
uh this assigned the scheme of our proposal i will go into it later but
uh just to right
say that uh one of the main uh
the important things of the of our proposal is that it's as and generic enough
so or the papers other proposals the specific techniques for occlusions to drop can be
already included here
now i will uh briefly overview of our particle filtering works uh just a set
of particle filtering use of a would method which is able to um follow the
objects in the scene um and we uh make use of five tracking-bydetection by reading
this means that we have and detections with our which are painted in general we
have the tracks which are painted in
blue in this case and uh with the corresponding id and a set of particles
which are the ones that are distributed along the frames in order to search for
the new appearance of the of the truck
particle filter has two ways that's the first one is the prediction also known as
a importance function this is the equation of the importance function um
and here you see the main components of these equations this is this will be
used later in our formulation uh it's a normally based on human detector are for
example whole class svm the historic oriented gradients and a classifier uh the dynamics pdf
uh for example in our case we use a random walk but we could use
of our higher-order approaches an upright which is open a predefined way to search for
the um new occurrences of the truck
the second is that if the is the correction which is the weighting of the
particles uh in this case we use a color model very simple is the colour
histogram of the of this torso region of the pedestrian
and that you see here uh we have uh the particles are and painted according
to the weighting of the of the of the colour matching so the brighter the
particle the highway
uh this is a proposal of we propose a
state based tracking we have potential tracked occluded lost states and also the additional that
when we keep the track
and now we will explain each one of the uh specific a steps not all
the details but uh the main important components that uh will highlight why this proposal
is useful
first um we have a detection in general and the first we make use to
create a new track which is painted in dashed blue and uh we initialize these
track with the state potential
after a set of frames if and their track has corresponding detections we obtain that
the state from potential to track
otherwise if the track does not have enough corresponding detections in the next frames we
give a soul goes from potential to that
then in the case that the person is correctly tracked so out of the results
of are several uh frames there's a corresponding detection we can apply frame-by-frame corresponding actions
specifically them at the track state this is an example there in a set of
actions for example the weighting of the particles gives an initial model used in the
data association between detections and the track and i will just highlight uh for model
of time their importance function which is how we draw we distribute the particles in
the frames in the case in this case uh we only use the dynamics without
thinking and talk on the detections in order to deceive it is practical so as
you see here we from the original equation we only use one part
then um you know to make the transitions
uh from track to do that and two lost we propose to conditions are quite
simple the first one is that the isolation of the tracks so if two tracks
overlap each other
uh in this case we use that pascal a criterion that pascal overlapping criterion is
also known the jaccard overlapping criterion is quite simple and the other um condition is
the classifier um and studied in this case we use a online classifier would uh
more than is the colour of the course of the person and
we track it a long time and in the case that we see in the
yellow uh line we see that tracked person in which the confidence on the of
the online model uh is maintained a long time
in a real we see the case of an occluded target in which there and
confidence of the of the color model and jumps down so we detect this uh
first we filter the signal which someone the kalman filter and then we test these
and jumps with the generalized likelihood ratio test
so in the case that and these conditions are fulfilled so the um pedestrian the
person is not isolated and there's a jump in their classifier we go to the
occluded uh
state
and so one here we have a additional uh conditions from occluded to track come
from the same occluded to this to the occluded the state and
i highlight this again the importance function in this case is based on detections so
when the truck uh when the person disappears which because it so you the by
another person we go to the occluded and the state and the particles are not
drawn according to i don't know what is it wasn't importance function of the track
state but are drawn according to the detections around
and so one so um we have the same conditions with the same tests in
order to make the transitions between the states in this case uh we see that
the lost target is occluded by their scene
and so on
and we finally if a target has been lost for different a different set of
frames week
to sum up um these are the experimental results um we have a me to
use of three datasets the tud-crossing to decompose and the one on pets two thousand
and nine and compared to the original um formulation of the particle filtering and our
proposed state based
one and give several statistics
the first ones are the with the object tracking precision and accuracy in the case
of the precision it evaluates the uh overlapping between the ground truth and the track
itself and we see in its around plus or minus one percent but this is
not significant if since uh we have um already detected the person but there's a
slight displacement with respect to the position of the tracks with respect to the ground
truth
uh the one which is a really significantly fees the accuracy in this case with
our proposal we gain about seven percent and also the false negative rate on the
false positives pretty much which are also decreased in our proposal and finally the identity
switches this is when a truck has to be really se reinitialised and given a
different id from the one but it had in our case uh the number channel
and then i show you some examples
and this case we have attract a pedestrian which is isolated and it works quite
well both with the original proposal and on the with the original approach and the
state based approach we see uh the potential track which is initialized then it uh
divorced what tracked track then uh you can see she has several detection so now
false positives will which will disappear but the track is currently uh track i don't
time so there's no problem this case
in the case in which we really see that the differences between the traditional approach
in our proposal is in this case for example have an occluded uh case we
have to target which overlap each other and in the first case the um person
on the front high yets the person on the back so the model uh drifts
and it gets a stark on the background and finally there's a new uh track
for the new detection and then new id for these same person so he would
have a switching in the idea
in our case uh the same happens the two targets okay with each other but
the system is able to reinitialize the track with the same id by detecting that
in the third frame in this case and there's an occlusion
and finally the case in which a track has been lost in this case the
track is computed by the background but the scene um uh by the person but
the our system is able to detect that it hasn't lost and it finally uh
detect that in matches the track that have we lost with a new detection in
the system that colour model matches and it we initialize the tracker and with the
same i
so to sum up the conclusions are that we have presented a and the state
based uh tracking approach which is able to deal with it got tracking problems that
are their creations that hijacking of the target one and drifting at sea trial it
uh gives a performance improvements with respect to the traditional on the state based approach
it's applicable to different existing approaches such as uh or occlusion classifiers specifically some classifiers
are used we saw that kind of filter in the test and one with a
different state of the art
uh_huh lost target detections the could be actually included in our proposal and especially in
the future work um since this is a on uh mutual to be framework this
is we have make use of very simple ingredients so for example the um the
colour model for the for the person is quite simple socially going to more advanced
that um
components for instance appearance models for the for the for the tracks or an increasing
the number of particles depending on the state of the person uh we think that
it would uh provide a better results
thank you
yeah
uh experimentally there are uh
as many as the ones you would use for a non state based approach but
multiplied by the number of states
uh_huh
and then they are related with the transitions for the for example for the isolation
uh we use the syllable five which is typically used uh
for the overlapping and for the classifier itself it's also i just the bike and
by looking experimentally
no not at the moment we uh have one frame per second
yeah
for
the case is my
ten people more or less yeah can people yeah
to become an attempt works
yeah in fact it would because uh the particle filtering uh allows you to process
independently the different uh tracks and seems uh each one of the states allows you
to um to make use of different parameters depending on this date you could spend
less particle so less processing on the trucks that for instance are isolated so by
accelerating these things ending in these things we can we think that it would be
to reach we have then
yeah
it is based on the chance writing
or tracking on particle filtering
yeah
yeah i don't know which one but it's based on that
okay fine
a speaker