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This article will explain in detail how to use Python to build AI cars and pedestrian tracking for beginners. I think it is very practical, so I share it with you as a reference. I hope you can get something after reading this article.

Step 1: get a lot of pictures of cars

Step 2: turn them all into black and white pictures

Grayscale images make the algorithm faster. Color increases the complexity of the model, or we can say that gray images are used to simplify mathematics. For example, we can talk about brightness, contrast, edges, shapes, contours, textures, perspectives, shadows, etc., without talking about colors.

Step 3: train algorithms to detect cars

Now the question is: how do computers train algorithms?

We just found a match.

We can match the above functions to actually detect the car's rear bumper, as shown below.

The idea of testing pedestrians is the same.

It's all about matching features or shapes. If an object matches the above characteristics, the model will detect it as a pedestrian.

Let's start writing detectors.

Step 1: we first need to install the OpenCV library.

Pip install opencv-python

If this doesn't work, try:

Pip install opencv-python-headless

If you are still unable to install. Try to use Google search, how to install opencv on your computer?

Step 2: download the machine learning file (Haar Cascade xml file):

We have provided a pre-trained car and human (pedestrian) classifier, we just need to download it.

Automobile pre-training Classifier: https://raw.githubusercontent.com/andrewssobral/vehicle_detection_haarcascades/master/cars.xml

Human pre-training classifier: https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_fullbody.xml

Step 3: we only need to write 20 lines of code. You can understand the code by reading it.

This is the end of this article on "how to use Python to build AI cars and pedestrian tracking for beginners". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, please share it for more people to see.

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