I'm trying to create an application that will be able to track rapidly moving objects in video/camera feed, however have not found any CV/DL solution that is good enough. Can you recommend any computer vision solution for tracking fast moving objects on regular laptop computer and web cam? A demo app would be ideal.
For example see this video where the tracking is done in hardware (I'm looking for software solution) : https://www.youtube.com/watch?v=qn5YQVvW-hQ
Target tracking is a very difficult problem. In target tracking you will have two main issues: the motion uncertainty problem, and the origin uncertainty problem. The first one refers to the way you model object motion so you can predict its future state, and the second refers to the issue of data association(what measurement corresponds to what track, and the literature is filled with scientific ways in which this issue can be approached).
Before you can come up with a solution to your problem you will have to answer some questions yourself, regarding the tracking problem you want to solve. For example: what are the values that you what to track(this will define your state vector), how are those values related to one another, are you trying to perform single object tracking or multiple object tracking, how are the objects moving( do they have a relatively constant acceleration or velocity ) or not, do objects make turns, can objects also be occluded or not and so on.
The Kalman Filter is good solution to predict the next state of your system (once you have identified your process model). A deep learning alternative to the Kalman filter is the so called Deep Kalman Filter which essentially is used to do the same thing. In case your process or measurement models are not linear, you will have to linearize them before predicting the next state. Some solutions that deal with non-linear process or measurement models are the Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF).
Now related to fast moving objects, an idea you can use is to have a larger covariance matrix since the objects can move a lot more if they are fast, so the search space for the correct association has to be a bit larger. Additionally you can use multiple motion models in case your motion model cannot be satisfied with only one model.
In case of occlusions I will leave you this stack overflow thread, where I have given an answer covering more details regarding occlusion handling in case of tracking. I have added some references for you to read. You will have to provide more details in your question, if you would like to receive more information regarding a solution (for example you should define fast moving objects with respect to camera frame rate).
I personally do not think there is a silver bullet solution for the tracking problem, I prefer to tailor a solution to the problem I am trying to solve.
The tracking problem is complicated. It is also more in the realm of control systems than computer vision. It would be also helpful to know more about your situation, as the performance of the chosen method pretty much depends on your problem constraints. Are you interested in real-time tracking? Are you trying to reconstruct an existing trajectory? Are there multiple targets? Just one? Are the physical properties of the targets (i.e. velocity, direction, acceleration) constant?
One of the most basic tracking methods is implemented by a Linear Dynamic System (LDS) description, in concrete, a discrete implementation, since we’re working with discrete frames of information. This method is purely based on physics, and its prediction is very sensitive. Depending on your application, the error rate could be acceptable… or not.
A more robust solution is Kalman’s Filter, and it is pretty much the go-to answer when tracking is needed. It implements prediction based on all the measurements obtained so far during the model's lifetime. It mainly works on constant-based measurements (velocity and acceleration) although it can be extended to handle non-constant models. If you are working with targets that won't exhibit a drastic change in their velocity, this is what you (probably) should implement.
I'm sorry I can't provide you with more, but the topic is pretty extensive and, admittedly, the details are beyond my area of expertise. Hopefully, this info should give you a little bit of context for finding a solution.
The problem of tracking fast-moving objects (FMO) is a known research topic in computer vision. FMOs are defined as objects which move over a distance larger than their size in one video frame. The solutions which have been proposed use classical image processing and energy minimization to establish their trajectories and sharp appearance.
If you need a demo app, I would suggest this GitHub repository: https://github.com/rozumden/fmo-cpp-demo. The demo is written in OpenCV/C++ and runs in real-time. The authors also provide a mobile app version, which is still in testing mode. Using this demo app you can track any fast moving objects in real-time without even providing an object model. However, if you provide object size in real-world units, the app can also estimate object speed.
A more sophisticated algorithm is open-sourced here: https://github.com/rozumden/deblatting_python, written in Python and PyTorch for speed-up. The repository contains a solution to the deblatting (deblurring and matting) problem, exactly what happens when a Fast Moving Object appears in front of a camera.
I am working on a machine learning scenario where the target variable is Duration of power outages.
The distribution of the target variable is severely skewed right (You can imagine most power outages occur and are over with fairly quick, but then there are many, many outliers that can last much longer) A lot of these power outages become less and less 'explainable' by data as the durations get longer and longer. They become more or less, 'unique outages', where events are occurring on site that are not necessarily 'typical' of other outages nor is data recorded on the specifics of those events outside of what's already available for all other 'typical' outages.
This causes a problem when creating models. This unexplainable data mingles in with the explainable parts and skews the models ability to predict as well.
I analyzed some percentiles to decide on a point that I considered to encompass as many outages as possible while I still believed that the duration was going to be mostly explainable. This was somewhere around the 320 minute mark and contained about 90% of the outages.
This was completely subjective to my opinion though and I know there has to be some kind of procedure in order to determine a 'best' cut-off point for this target variable. Ideally, I would like this procedure to be robust enough to consider the trade-off of encompassing as much data as possible and not telling me to make my cut-off 2 hours and thus cutting out a significant amount of customers as the purpose of this is to provide an accurate Estimated Restoration Time to as many customers as possible.
FYI: The methods of modeling I am using that appear to be working the best right now are random forests and conditional random forests. Methods I have used in this scenario include multiple linear regression, decision trees, random forests, and conditional random forests. MLR was by far the least effective. :(
I have exactly the same problem! I hope someone more informed brings his knowledge. I wander to what point is a long duration something that we want to discard or that we want to predict!
Also, I tried treating my data by log transforming it, and the density plot shows a funny artifact on the left side of the distribution ( because I only have durations of integer numbers, not floats). I think this helps, you also should log transform the features that have similar distributions.
I finally thought that the solution should be stratified sampling or giving weights to features, but I don't know exactly how to implement that. My tries didn't produce any good results. Perhaps my data is too stochastic!
I've been tasked to carry out a benchmark of an existing classifier for my company. The biggest problem currently is differentiating between different type of transportation's, like recognizing if i'm currently in a train, driving a car or bicycling so this is the main focus.
I've been reading alot about LSTM, http://en.wikipedia.org/wiki/Long_short_term_memory and its recent success in handwriting and speech-recognition, where the time between significant events could be pretty long.
So, my first thought about the problem with train/bus is that there probably isn't such a clear and short cycle as there is when walking/running for instance so long-term memory is probably crucial.
Have anyone tried anything similar with decent results?
Or is there other techniques that could potentially solve this problem better?
I've worked on mode of transportation detection using smartphone accelerometers. The main result I've found is that almost any classifier will do; the key problem is then the set of features. (This is no different from many other machine learning problems.) My feature set ended up containing time-domain and frequency-domain values, both taken from time-series sliding-window segmentation.
Another problem is that the accelerometer can be placed anywhere. On the body, it can be anywhere and in any orientation. If the user is driving, is the phone in a pocket, in a bag, on a car seat, attached to a suction-cup window mount, etc.?
If you want to avoid these problems, use GPS instead of the accelerometer. You can make relatively accurate classifications with that sensor, but the cost is the battery usage.
A general question: is it possible to retrieve information about how far away e.g. a computer is from a wifi-router. For instance I want to get data on my computer if I'm 10 meters away from my home-wifispot or 2 meters.
Any idea if that is even possible?
Edit: How about bluetooth? Is it possible to get information about how far away bluetooth-connected devices are one from another?
I would recommend a measuring line or just good-old-fashioned guesstimating.
There is no "simple" way to do it (complex ways may involve building "accurate" signal maps ahead of time or trying to fit a better equation which is still subject to anumber of the limitations with the naive rule) and the rule of thumb "1/r^2" is just that -- a general rule of thumb. On the other hand, perhaps there is some existing software that will show you your RSS strength and make the task feel accomplished :-)
You will find useful links if you google for "RSS signal distance". This kind of task seems quite a common topic in academia w.r.t. small wireless devices ("motes") as well and there have been some interesting approaches to this problem such as the fitting of secondary low-frequency acoustic sensors.
You can query the signal strength which is some kind of indication of distance and obstructions and a few other factors all rolled into one measure. With just plain wifi though this isn't possible directly.
Try measuring the response time of the router to pings, with the data rate set to constant to avoid that effecting the response time. Take lots of samples and remove outliers to reduce errors, but you will still have a substantial quantization error. Subtract the latency of the router and computer, divide by 6 then multiply by the speed of light and hopefully you will have the distance to a resolution of a few metres.
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Closed 12 years ago.
how to expand spinner time ?
From The Nature of Time:
The Nature of Time
We have all been asked the question
'what is the time?', but this entry
will be addressing the subtly
different question: 'what is time?'
Many people, on being asked this
question, would probably say that they
don't have the time to answer. In this
entry, we will explore many theories
about the nature of time. No one
theory has unquestioning truth about
it, so it will be up to you to decide
on the one you see is best.
The Classical Approach to Time
There is not very much to say on this
theory since it is the one with which
we are most familiar. Traditionally,
time is simply seen as a measure of
the distance between events. It has a
past, present and a future. The past
is considered to have already happened
and to be unchangeable, while the
future is considered to be open to
many possibilities. Humans measure
time using many units, some based on
real events like the rotation of the
Earth, others that are even more
arbitrary.
Isaac Newton's classical description
of time in his highly-regarded work
Principia is that it 'flows equably of
itself', which means that time 'flows'
at a constant rate that is the same
for everybody - it is independent of
the events that take place in it. It
would be untrue to say that this idea
was unchallenged until the twentieth
century - the 18th-Century empiricist
philosopher George Berkeley, for
example, disagreed with Newton and
held that time was 'the succession of
ideas in the mind' - but there was no
serious evidence to suggest that
Newton's elegant and absolute
description was wrong until Einstein
destroyed it.
Unfortunately, the classical view of
time is biased towards the human
perception of the 'flow' of time. We
see events in one direction, and we
assume time to be the same everywhere.
The classical approach to time does
not explain exactly why we perceive
time in this way, and it does not
describe how the effect is achieved.
The other theories of the nature of
time challenge the very roots of this
natural point of view.
Relativity
The Theory of Relativity is the
celebrated discovery of the physicist
Albert Einstein. Originally, it was
two theories: the Special Theory of
Relativity came first in 1905 and
states that the rate at which time
passes is not the same all over the
universe - it is dependent on the
observer (in other words, it is
relative). It is not hard to see that
different people perceive the passing
of time at a different rate to others:
as we get older, less information is
processed about our surroundings per
second, so we perceive time to be
going faster.
But Einstein's theory went further
than this. The relativity of time is
based not on our age, but on our speed
of movement through space. The faster
we travel through space, the slower we
travel through time. Although this
sounds crazy at first, it makes sense
when thought of in a particular way.
The theory of relativity demands that
we view space and time not as separate
entities but as one concept called
space-time. Time becomes a fourth
dimension, just like the other three
dimensions of space that we are used
to (height, width and length). This
view of time is crucial to
understanding most of the other
theories about time's ultimate nature.
Humans only possess two-dimensional
retinae (the light-receptive surface
at the back of our eyes), which means
that we can only see in two
dimensions. Our vision of the third
dimension is a result of perspective
and the existence of our binocular
vision. If we had three-dimensional
retinae, we would be able to see all
of an entire room simultaneously - its
walls, its floor and its ceiling at
the same time! For this reason, it is
very difficult, if not totally
impossible, for humans to visualise a
fourth dimension.
To overcome this impairment, it is
useful to use lower-dimensional
analogies when talking about
dimensions above three, even if we are
talking about time as one of these
dimensions. So in this case, let us
imagine that the universe is shaped
like a cuboid1, and that humans are
two-dimensional and have
one-dimensional retinae. Imagine that
the spatial dimensions are the width
and height of a cross-section of the
cuboid, meaning that humans can move
up, down, left and right at will
within the cuboid. Imagine that the
depth of the cuboid is time.
Right, now imagine that you are a
two-dimensional human within the
cuboid and that you start off being
midway up the cuboid. Then you start
moving upward (ie, through space, but
not time). Eventually you hit the edge
of the cuboid. Now imagine that you
move downwards, but that you also move
through time in a forward direction.
This time it will take you longer to
get back to being mid-way up the
cuboid because you are not taking a
direct route downwards - you are also
moving through time. As you can see,
moving through time slows down your
movement in space.
It works the other way around too. If
you stay still in space and just move
forward in time, then it will take
less time to get to a particular point
in time than if you move upwards and
forwards in time simultaneously. So
movement in space slows down your
movement in time. This is what
relativity states about how time
really is. However, the amount by
which time is slowed down when you
move through space is very small in
everyday situations, and you would
need to move at a speed of a
considerable percentage the speed of
light in order for it to make any
noticeable difference.
Relativity has been proven too. Atomic
clocks2 have been placed in aeroplanes
moving at high speeds and then
compared with clocks that were on
Earth. Slight differences that are
exactly predicted by the mathematical
equations of relativity were indeed
detected.
The general theory of relativity goes
a step further and was published in
1916. Einstein stated that mass curves the 'fabric' of space-time to create
the illusion of the force of gravity.
Again, a lower-dimensional analogy is
best. Imagine putting bowling balls on
a sheet of rubber. They bend the
rubber. Any object coming into the
vicinity of the curve begins to move
around the curve like something
spiralling around a sink basin.
Einstein's picture of gravity is that
simple. And again, this has been
proved. Einstein made predictions
about how light would be taking
technically curved paths around large
masses, and this effect was measured
during a total eclipse of the sun.
Time and Determinism
You will have noticed that the theory
of relativity does not carry any
description of a 'flow' of time, and
in fact, it describes time in almost
exactly the same way that we are used
to thinking about space. Relativity
unifies space and time. All points in
space are in existence simultaneously
- this is common sense; so are all points in time in existence
simultaneously too? This would suggest
that all events in time are already
'here' and that there is no scope for
choosing what happens in the future.
This view of time is called
determinism because events are
pre-determined.
It is worth noting that relativity
does not rule out the idea of free
will, but does not provide any support
for it either. Many people can get
upset about the evidence supporting
determinism because humans like to
think they have a free will to make
independent decisions. Such people
would not feel better if they heard
about the many worlds theory of
quantum mechanics.
Time in the Many Worlds Theory of
Quantum Mechanics
To understand this theory, we need to
go back to our cuboid example. You
will notice that each event in time is
simply a cross-section of the cuboid
(a square). Humans effectively
perceive the dimension of time in this
cuboid to be a succession of these
squares. Like frames in a movie, these
create the illusion of a smooth
passage of time. But why is it that we
see time like this? The answer to this
question will be explored later.
If you think about the world around
you, you will most likely notice that
it seems to have been tailor-made for
life. The universe has the precise
properties that led to the formation
of life on Earth. For example, in the
early universe there was a 'battle'
between matter and anti-matter3. The
particles with the certain quantum
properties that we now characterise as
'matter', for a hitherto inexplicable
reason, won the battle. If this hadn't
happened, we could not exist, or we
would not be the same as we are today.
Many physicists have speculated that
this and other similar events are too
much of a coincidence to be regarded
as just that: a coincidence.
Martin Rees, the Astronomer Royal of
the UK, paints an analogous picture of
going into a clothes shop. If you go
into a clothes shop that only sells
one size of clothing, it would be a
big coincidence if you found it was
your size. However, we get no surprise
when finding our own clothes size in a
clothes shop because good clothes
shops sell a wide range of clothes
sizes. We can now extend this picture
to the universe. It is very unlikely
that the universe should exist because
of how biased it seems to have been
towards gravitational stability and
the creation of diverse life later on.
However, if we see the universe as
providing a wide range of 'universes'
of different properties, it will come
as no surprise if we find one universe
that supports life.
You can think of this theory as
multiple cuboids in a vast universe of
cuboids, all with their own
space-time. Each cuboid represents one
universe that has a different set of
laws of physics, and therefore could
be wildly different from all the other
universes. There may in fact be a
large number of universes that support
life but with small differences, just
as there might be many shirts of your
size in the clothes shop, but perhaps
in different colours.
In this view, there are multiple
timelines. Some people have likened
this view of time to train tracks. We
move along a train track in one
direction, but there are huge numbers
of other train tracks running parallel
to ours. Each train track may be
different in some way (it might have
trains on it, for example). For this
reason, the other universes around us
in this 'multiverse'4 are referred to
as parallel universes.
A multiverse of space-times is not
just a theory that solves the question
of why our environment is so suited to
life; it is also a theory of quantum
mechanics. In the quantum theory there
are many events that take place
because of random chance. In electric
currents, for example, the electrons
that make up the current follow a
random path in the wires that is
influenced by a fields of electrical
forces they pass through, which is why
it always seems that the current is
split 50:50. Many physicists believe
that with each quantum decision like
this, every possibility has a separate
universe in which it is enacted.
Hence, in one universe the electron
goes one way; in another, it goes the
other way.
In this theory - which is called the
many worlds interpretation of quantum
mechanics - every possibility gets
enacted. Since quantum interactions
are the fundamentals of any larger (or
'macroscopic') reaction, we can infer
that everything happens in one
universe or other. So if you have a
decision to make, say whether to take
a holiday to Hawaii or not, there is
one universe where you go, and one
universe where you don't. This also
spells trouble for free will. All
possibilities get played out, so it is
just a matter of which universe you
are in to determine which way you go.
There is a variation of this theory.
For this variation we will need to
think another dimension lower. So,
instead of imagining universes as
cuboids, we need to imagine them as
rectangles. Imagine the length of the
rectangle is time; and its other
dimension, space. The rectangle has no
thickness whatsoever, so if you put
multiple rectangles (ie, multiple
universes) on top of each other, the
whole structure becomes no thicker.
This version of the many worlds
interpretation is slightly easier to
grasp, because otherwise we would have
universes branching off from one
another to eternity, which is rather
difficult to imagine. There is no real
evidence for or against either of the
theories of the multiverse,
unfortunately.
You will have noticed that in all
these theories, time has two
directions just like all the other
dimensions. In theory, there is
nothing to stop us from moving in the
other direction. There is another
slightly different theory of time as
being bi-directional, and you might
also be interested to see how this
could lead to possibilities of
time-travel.
Why Do We Perceive Time the Way We Do?
What, then, is time? If no one asks
me, I know what it is. If I wish to
explain it to him who asks me, I do
not know.
- St Augustine. If time is a dimension just like all the others,
why do we experience it so
differently? This is the question that
interests James Hartle of the
University of California in Santa
Barbara, along with physicists Stephen
Hawking, Murray Gell-Mann and Steven
Weinberg. They believe that the
passage of time is just an illusion.
Hartle thinks that time's arrow is a
product of the way we process
information. Gell-Mann gave creatures
that process time in this way the name
'information gathering and utilising
systems' (IGUSs). Humans are IGUSs.
Because of our two-dimensional
retinae, we can't take in multiple
cross-sections of the 'cuboid' - ie
'frames' of time - simultaneously. We
gather information about this frame -
our surrounding environment - using
our senses, and then we store the
information in an input register. This
does not have an unlimited capacity,
so we have to transfer the information
to our memory registers before we can
input the information about the next
frame. Humans have a short-term and a
long-term memory, as well as our
cerebellums that store 'unforgettable'
information (such as how to swim).
IGUSs also carry something called a
'schema', which is a generalised model
of our perception of our environment.
It holds several rules about what is
best to do and what is not a good idea
to do. The information we receive from
our surroundings is passed to the
schema to determine how we react in
certain situations. The decision is
conscious, but we also do unconscious
computation of information: the schema
is updated unconsciously. The
conscious part of the IGUS in humans
focuses on the input register, which
we call the present. The unconscious
part focuses on information in the
memories, and we call that the past.
This is why we consciously experience
the present and remember the past.
The movement of the information
through the IGUSs registers creates
the illusion of the flow of time. It
is not time itself that flows. Each
IGUS has a different speed for the
flow of its information between
registers. This corresponds to
differences between the perception of
the speed of the flow of time. Flies,
for example, need to process more
information per second in order to fly
so quickly but still avoid common
obstacles; therefore, they perceive
time as going slower. To us, a fly's
perception of time would look like
slow motion. Flies only live for a few
days, or a few weeks as a maximum, and
to a human, this is a very short
lifetime. But to a fly, this feels a
lot longer.
So the reason that we experience a
'flow' of time could just be because
of how we process information. It is a
competitive advantage to us as a
species to process information bits at
a time. It wouldn't make sense for us
to have evolved with the capability to
see all time simultaneously.
Digital Time, or, Is Time Like a
Movie?
You may have noticed a reference to
'frames' of time in the explanations
above. We usually think of time as
continuous - a smooth passage of
events. However, most physical
theories define space and time as
being the opposite of a continuous
passage of events. M-theory and Loop
Quantum Gravity, for example, are both
serious scientific theories (not
proven theories, though) that state
that space and time have minimum
units. There was even a theory of
quantum mechanics to suggest that time
was made of particles called
'chronons'!
The theorised minimum length of time
possible is called the Planck time and
is equivalent to 10-43 seconds. When
space or time is 'digital' like this,
we say that it is 'discrete'.
If this theory is true, then our
perception of time could be like a
movie. Movies are not continuous: if
you slow them down enough, you see
that they are just collections of
still photographs played in quick
succession. We process information
about our surroundings and obtain a
picture just like one frame of a movie
or animation. When 'played' in quick
succession, this creates the illusion
of smooth, continuous movement.
Is Time Really That Much Like Space?
So far, time has been seen as very
much like a dimension of space, and
its passage in one direction has been
seen as an illusion. But there are
some counter-arguments; there are
still some big differences between
time and space that cannot easily be
explained as illusions.
One way of supporting the idea that an
'arrow of time' is irrelevant is by
proving that all processes are the
same if done forwards or backwards. In
quantum mechanics, most interactions
between particles are 'time-symmetric'
- it doesn't matter whether you look at them from past to future or future
to past because they look the same.
But this is not true of macroscopic
objects. Wine glasses shatter, but you
rarely see shards of glass assemble
themselves into wine glasses.
Physicists can explain why shards of
glass do not form wine glasses by
postulating the existence of 'the
thermodynamic arrow of time'.
Thermodynamics is basically a
collection of laws. Here is how the
chemist PW Atkins summarises them:
There are four laws. The third of
them, the Second Law, was recognised
first; the first, the Zeroth law, was
formulated last; the First Law was
second; the Third Law might not even
be a law in the same sense as the
others. The gist of it is that the
universe is always becoming more
disordered. The disorder of the
universe is called 'entropy', so we
say that entropy is always increasing.
Nobody really knows why this is the
case, but we see it all the time in
experiments. This is why heat always
flows into colder areas, but never the
other way round. Heat is simply the
result of giving particles in a given
system more energy; they begin to move
and vibrate randomly, which is a
disordered state. Colder things are
more ordered because their constituent
particles tend to be harder to move.
This in-built arrow explains why
macroscopic objects have irreversible
interactions. This is a clear
difference from space. If you think of
the spatial manifestation of a table,
it does not follow that one end of the
table is more disordered than the
other, but it does follow that the
table will end up more disordered in
the future than when it has just been
made. Hence, there is a very distinct
difference between time and space.
Can Time Be Reversed?
If time's 'flow' in one direction
really is an illusion, what is there
stopping us from reversing it? In
theory, nothing! Lawrence Schulman of
Clarkson University in New York
thoroughly believes that time can run
backwards. In other words, shards of
glass can turn into wine glasses,
people grow younger and younger and
the universe gets smaller and smaller.
In fact, Schulman goes as far as to
say that such reversed-time zones can
exist as spaces within our own
universe. A computer simulation has
shown that regions with opposite time
arrows do not cancel each other out
and do not disturb each other at all.
The great thing about this theory is
that if a civilisation in a
reversed-time region kept records of
events that occur in our future, the
records might have survived to our
past (which is their future). Finding
these records could tell us the
future. This is, of course, a long
shot, but still a physical
possibility.
Another possibility is that the
universe's arrow of time (as far as
thermodynamics is concerned) will
naturally reverse itself at a crucial
point in the history of the universe.
At this point, the universe would
start to get smaller and everybody
would get younger until there was a
big crunch analogous to the big bang.
This creates a perfect symmetry to the
universe.
Again, there is little evidence that
shows us that reversed-time regions
exist, and there is no evidence that
the universe's thermodynamic arrow of
time will naturally reverse itself.
Equally, there is little evidence
against these theories either.
So what is time? Is it a dimension
just like space? Does it flow, or is
that just an illusion? Is time digital
like the frames of a movie, or does it
flow continuously? And can time really
be reversed or manipulated? None of
these questions can be answered with
definite confidence, but next time
somebody asks you what the time is,
perhaps you'll think of the answer
differently.
Kernel::sleep ?