Udemy – Dynamic Analysis Using Python and OpenCV 2023-3 – Download

Explanation

Motion Detection using Python and OpenCV is a complete step-by-step tutorial for implementing automotive desktops and social distancing. Motion detection is a subfield of computer vision that aims to detect motion in scenes or in real time. Such an application can be very useful especially for security systems in which suspicious activities such as a thief trying to enter the house must be identified. There are several other applications such as: traffic analysis on roads, people detection/counting, livestock tracking, bicycle counting, etc. A traffic control system can use these techniques to determine the number of cars and trucks crossing the highway at certain times of the day, in order to plan road maintenance.

What you will learn in this course:

A basic understanding of background subtraction applied to motion detection
Implementation of MOG, GMG, KNN and CNT algorithms using OpenCV as well as quality and performance comparison
Improve the quality of the results by using anti-processing techniques such as morphological operations and blur.
Implement a motion detector to monitor the environment
Run on social distancing
Implement car and truck tables using road maps

Who is this course for:

People interested in implementing motion detectors or object counters
Bachelor’s and Master’s students in computer graphics, digital image processing or artificial intelligence
Data scientists who want to increase their knowledge in computer vision