What is Computer vision, Image classification, object detection, how does object detection works, object tracking, how does object tracking work
COMPUTER VISION TECHNIQUES
When we say computer vision what does it come to our mind? and what are the applications of computer vision is?
All wondered about what's called self-driving cars which are the recent trend and which have become very popular these days we also have been amused about. So many other things of Artificial Intelligence and the applications where you get to know the automotive industry is one of the very beneficiaries and how to detect diseases in agriculture or the healthcare industry and how do you use it in criminal cases to solve them earlier. These are a few uses of computer vision and related artificial intelligence technology.
What is computer vision?
Computer vision is a sub-branch of machine learning which is used to give
the visual perspective to computers and mobile phones. In this era, machine
learning and computer vision are currently having a big impact on the market
right now. Computer vision is a technology that gives visual systems to
computers and mobile phones which a human can do like recognize the objects
like a car or a human person or a dog.
Computer vision is a complex interdisciplinary field that deals with how
computers can get to comprehend the world from images or videos. It
seeks to understand and automate tasks that human visual systems can do.
It is a field of artificial intelligence that trains computers to derive
information from digital images, videos, and other visual inputs- and make
suggestions based on that data.
Computer vision gives the visual system that can recognize the
objects from digital images and videos. Then react according to it,
if there is a car it can recognize which company's car is it.
There are major five techniques in computer vision which are given below:
1.
Image classification
2.
Object detection
3.
Object tracking
4.
Semantic segmentation
5. Instance segmentation
Image classification
Image classification is a technique through which we can input the image in
the computer. It will recognize what the object is like it is a human or
a dog or a car may be or vehicle or something. It is a supervised learning
problem, defines a set of target classes (objects to identify in images),
trains a model to recognize them using labeled example photos.
How does the image classification work?
So image classification is used to compare and identify objects from the
image. It takes a lot of images of a category like if we need to detect a
vehicle, take many images of the vehicles from different angles and
different perspectives. Then label it with the tag and create a data set to
train a classifier. The dataset is prepared with a lot of images labeled
with only a single category of object. Like this, we take images for
different categories with a single labeled category.
Then train a machine learning computer vision model out of it for the image
classification and then pass the image which is not even in the training set
we have given. It will compare the input image with all the images which are
labeled and train the model with and give the prediction if it is the
vehicle or not. In the last step, check the classifier’s accuracy by
giving it the images to predict which haven’t been included in the training
dataset. Then compare new images with the labeled images to check the
prediction accuracy.
But the limitation of image classification is that it only detects and only gets trained on a single object and a system sometimes can have inaccurate predictions.
Object Detection
Object detection improves the limitation of image classification.
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 in images and videos. Researched domains include
Scene text detection, face detection, Pose detection, and
pedestrian detection. This method is used for detecting many objects from images/videos or
real-time camera feed.
How does object detection work?
Labeling many objects in the images and train the data set out of it.
Then create the model from that data set. Bounding boxes are created around
the multiple objects are labeled in the images, unlike image classification
where the single category is labeled in the image. Taking multiple images
and annotate many objects in all the images and train the detection
model using the dataset with the help of object detection framework. Then, check the model by running the result on it with the test images for
checking the accuracy of the model. And predict the images it can detect
many objects.
Object Tracking
Object tracking is an enhanced version of object detection that works on
real videos or in the real-time feed in the live camera. The feed from video
or camera is split into different frames at the runtime and each and
everything is given into the Computer Vision model for prediction of the
object and tracking.
How does object tracking work?
The object detection technology or method works on the images and videos. But it cannot track the motion of the person very efficiently. So what does
object tracking does it gets the input from the video or the live camera
feed and divides it into the little frames and per frame. It passes the
image frame into the model which was trained earlier and then it gives the
output. It is very efficient and fast than object detection. The data set
creation is like object detection.
The datasets are created and trained using annotation (by creating bounding
boxes) around the objects in the images. Further, each of the similar objects is given a uniquely labeled class that is used afterward for the prediction of the object at the time of tracking.
For example, you can see the detection like the vehicles are moving and
the people are moving. So it is tracking the objects where the car is
going and or where the person is going.
To be continued...
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