Use Computer Vision to Read Number Plates From Videos

Detecting License Plates, Using Computer Vision

Given images and videos of cars, Reckoner Vision will exist used to find out what a license plate looks like and how to detect i.

Chris Zaire

Phtot by Damir Kopezhanov from Unsplash

Reckoner Vision is a field of data science that is different whatsoever other. Instead of dealing with datasets they bargain with images. In the aforementioned way that Neural Networks try to procedure information like the human brain, Computer Vision attempts to see, identify, and process images in the style that human vision does.

In this commodity we're going to discover 1 of th east near used cases of Computer Vision, detecting license plates. They are singled-out and easily recognizable, which makes them easy for the estimator to pick up and spot. Merely first nosotros have to let the computer know what makes a license plate a license plate.

Cascade Classification

How nosotros tell the estimator what a license plate looks similar is that we feed information technology a few hundred samples of a license plate, and samples that aren't images of license plates. A classifier is then trained on these examples and volition output a 1 if the input image contains a license plate, a 0 if non.

The unlike classifiers of Cascade Nomenclature

Pour Classification is used equally the classifier considering it consists of several simpler classifiers (stages) that are applied after to a region of interest until at some stage the candidate is rejected or all the stages are passed. These uncomplicated classifiers detect the edges of the license plates, the lines in the plates and the surrounding features. If one or more of these classifiers cannot exist detected in the input paradigm it will decline the image and declare there is no license plate. Merely if all these features can be establish it will classify the image as having a license plate in it.

Feature Matching

To give you an idea of what Pour Nomenclature is doing, I'm going to testify you an example of feature matching. Exactly how it sounds characteristic matching finds the corresponding features from two images.

Down below is a picture of a license plate and the back of the machine. What feature matching is going to do is discover the features of the license plate that correspond to features in the paradigm of the back of the motorcar.

Ouput from Feature Matching

The image above is the output from feature matching the ii images. The lines evidence where/what features were mutual in both images. As nosotros tin can see nearly all the lines go to the license plate on the dorsum of the machine. This is essentially how Cascade Classifier works and how it can detect license plates in images.

The Issue

Subsequently we utilise Cascade Classification on the image, if a license plate is detected it will return the x, y location of where it was found in the epitome and the width and height of the plate. Now that we have the points in the image that show where the license plate is we can draw on the image, a rectangle effectually information technology. Using OpenCV, (a Computer Vision library) it has a function where we can draw rectangles on a given image, all information technology needs is the points of where it should be drawn.

And then from the 10, y, width, and acme outputted from Cascade Classifier we can change this image…

Original Epitome

To…

License plate is detected and now has a red box around information technology

But with those points we can practise even more applications. What nosotros can do is actually mistiness the license plate. This is often used in the existent world because Boob tube networks that testify cars can't legally show someone's license plate without their approval. And so what happens is that they blur the license plate so nobody can read it. Similarly to cartoon a rectangle, OpenCv has a function where given a set of points it volition blur the image in that location.

So our original epitome gets blurred and now looks like this…

After the license plate is blurred information technology tin no longer be conspicuously read

Detecting License Plates In Videos

To empathize how we tin can practice this, we have to understand what actually constitutes a video. A video is essentially just frames put in succession to perceive that something is moving. You can think of information technology like a flipbook, when the book is flipped the drawings appear to move fifty-fifty though it'southward merely the images being changed a piddling bit at a time. These frames that make upwardly the video are just images that ae slightly beingness changed. Therefore like nosotros did with the images above, all were going to do is utilise the same classifiers on every frame in the given video. If that frame has a license plate a red rectangle will be placed around it.

The video that is gonna be used is cars driving on the highway in traffic. To testify some more than applications of Reckoner Vision, not simply are we gonna detect the license plates of the cars in the video beneath, just we volition zoom in on the plates and display them higher up where each license plate was detected.

Note: Practise not look the classifier to pick upwards every license plate in the video. Y'all will see that when a motorcar is too far abroad it will non pick up that license plate because the features of the plate showtime to become less clear and fails the classifier.

Computer Vision in Video

Decision

Figurer Vision is dissimilar any other. Information technology is a nonstop growing field with data scientists and engineers finding new ways to create impactful visualizations. The case higher up was meant to give an introduction to those who had no idea what computer vision was and expose them to ideas of where it can get. Hopefully this inspires yous to learn more than nigh this fascinating field, like it inspired me to do this project.

If y'all're interested the lawmaking for this project can exist found on my GitHub: https://github.com/zita9999/License-Plates-and-Computer-Vision/tree/master

References

[1] M. Murataliev, White Auto, https://unsplash.com/photos/3Gg-98LaCxE

[2] G. Bjarki, Black Mercedes, https://unsplash.com/s/photos/pennsilvania-licenseplates

[three]4k Urban Life, 4K Car Driving Relax Video, https://beautifulwashington.com/

[four] OpenCV, Cascade Nomenclature, https://docs.opencv.org/two.4/modules/objdetect/doc/cascade_classification.html

walkerbeffele.blogspot.com

Source: https://towardsdatascience.com/detecting-license-plates-using-computer-vision-87b2f6d3e56e

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