Hi and welcome to the lecture on object detection in this section will basically be taking a high level
overview of the principles of object detection.
So let's get started.
So firstly, what is object detection?
Well, so far we've extensively dealt with image classification tests that is determining an image as
a whole to tell you what the image contains.
An example of that is basically the sofar dataset.
You can see these are all different images of airplanes, automobiles, birds and so on.
So an image classification test, you're just telling us what the images we're not seeing, what is
what are the objects in the image specifically like?
I know, I know we're saying it's a call, but we're not actually saying it's a car and a wall or a
bird and the tugboat behind it or a cage and so on.
So let's take a look a bit more closely at what object detection gives us.
So what if we wanted to individually classify the objects in an image like this?
You can see now we have calls with a bounding box around each car in the scene.
It's a bit hard to see the cause, but it actually works fairly well.
Then you have people being drawn over with this box and the bus as well.
So this is an example of an object detection algorithm, and it works quite well.
This is actually, I believe this is a field of vision tree are used here and it's working quite well.
And then you can see in that an example of it here.
It's identifying individual objects and drawing this building in boxes around them.
You have a ball club, mouse bottle of wine glass.
This is quite cool, isn't it?
An object detection is one of the most useful algorithms in computer vision.
It has such a wide range of applications.
And here's the last example here, which just gets a cup in an image here.
So you may be thinking this is hard and it is hard.
Take a look at this scene here.
There's so many different objects to identify.
So as you might rightfully infer, this is a challenging problem because we need to put two things localization
that is determining where the boundaries of this object lie.
So we need to figure out a way to draw a bounding box around object.
We have identified.
And then secondly, we need to classify what our object is.
So this one's a bulb, this one's a cup and a mouse.
So we need an algorithm, a computer vision, deep learning algorithm, preferably.
Actually, that's pretty much the only way we can do this.
We can use horror cascade classifiers, which you would have seen in the OpenCV U.S. to do detection.
However, those don't work out too well in the real world.
In many tests, and they're quite hard to trained.
So let's take a look a deeper look at up to the object detectors.
So optical directors are usually trained on one or more classes and is seek to produce a bounding box
around the object class that's been identified.
And there are many kinds of object detectors.
There are some nonlinear deep learning based ones, as I mentioned, our cascade classifiers.
And then also the sliding window at home, such histogram of gradients.
However, these preceded the very cool, deep learning methods that we use nowadays.
And these deep learning methods include things like RC and Estes YOLO, which is a very good detection
and detection too.
Well, it's a very good, efficient detect is actually quite good as well, and also rathlin it.
And then in the deep learning world, we have two main types of public detectors.
We have two shot, which uses two stages a region proposal stage and then a classification stage and
then a single shot that was localization and image classification at once.
So now let's take a look at the formal definition from Wikipedia for object detection.
So object detection is a computer technology related to computer vision and image processing that deals
with detecting instances of semantic objects of a certain class, such as humans buildings cause in
digital images and videos.
Well, research two means of public action, including face detection, which we have seen previously,
and pedestrian detection nowadays loads of other applications you can use object detection for, and
these include image retrieval, video surveillance and many, many others.
So now that you've got a deeper understanding of object detection, I want to take a look at something
called object segmentation.
This is so that you understand the difference between object detection and segmentation tests, so it's
related to object attention.
However, it sometimes relies on some very different algorithms to do segmentation.
So what exactly is object segmentation?
Well, segmentation firstly involves.
So level classification, and let's take a look at this example here.
So what this means here is that this is a person here, and you can see every pixel of the person is
highlighted in a red color.
And so far, you can see that other people in the scene are also in this red color.
Notice that the sidewalk has been identified as a class on its own.
And it's all been shaded pink and the has been identified and also been chained like a pinkish docket.
I think it's great and can see the cause being blue and trees being green.
So that's what pixel level classification is.
And this is an example of segmentation.
So let's take a look at the differences here.
So these objects in object recognition is image classification and object localization, which relates
to object detection.
And then afterwards, we have something called instant segmentation.
Now it is two types of segmentation this one called semantic segmentation that we see on the right to
notice the difference between semantic segmentation and instant segmentation.
Now, an instant segmentation, we have the individual sheep being classified and shaded in different
colors.
They are all going to be the same class as sheep.
But no, we have sheep one sheep, two sheep three and dog one.
Whereas previously with semantic segmentation, which is what we saw in the previous image here, that
was this one.
This was an example of semantic segmentation loops, so you can see that all the classes are the same
color and they all merge into one another.
So if you want to do some sort of fine grained analysis and allow analysis of individual classes like
this instance, segmentation that would be the method to use and you can see an example of it here.
So this is what I wanted to talk about.
So we have classification is just seeing that image has a cat classification.
Plus localization is actually during a bounding box around a cat.
Similarly, public detection is basically built on top of that same thing.
However, object detection can no do multiple classes and do multiple boxes in an image.
So generally, we always refer to this as object detection, and we don't really use classification
and localization as an algorithm and computer vision.
It's just the way to get to this.
And then we have instant segmentation and also semantic segmentation.
Instant segmentation is the more advanced vision of segmentation because we're now joined bounding boxes
around individual cats.
So like I said previously, object detection has a wide range of use cases.
This is a screenshot I took from the rubble of sight removal as as a very cool computer vision company
to have an annotation tool.
They need models to provide more so many different models that you can train.
It's really, really good.
And the guys who are behind our workflow always making new updates.
So I encourage you to check it out a little or a lot from these guys, and you can see how many different
tests.
Things like gas leak detection, flare stack monitoring, augmented reality, bean counting, which
is just kind of niche to be fit traffic onto portable IDs, soccer player tracking self-driving cars
the big one Remote Tech support, tennis line tracking, hard hat detection to something a lot of people
are doing nowadays.
Satellite imagery Logo ID Sushi Identifier I mean, that is an unlimited number of tests.
It's like as if you're a computer vision startup.
It's such a good thing just to go with an industry, talk to people, figure out what they would want
to use cameras to identify or to detect, like so many different things out there that you wouldn't
think about things like this and managing the equipment like just identifying like where suits and equipment
is using cameras is very useful.
So that's it for an intro into object detectors.
Next, we'll take a look at the history of Early Object Texas.
So we'll go to a quick overview on hard cascade classifiers and sliding window with histogram of gradients.
So stay tuned for that lesson.
Thank you for watching.