Motion Detection using Python and OpenCV

Motion Detection using Python and OpenCV, available at $74.99, has an average rating of 4.7, with 30 lectures, based on 50 reviews, and has 721 subscribers.

You will learn about Understand the basic intuition about background subtraction applied to motion detection Implement MOG, GMG, KNN and CNT algorithms using OpenCV, as well as compare their quality and performance Improve the quality of the results using pre-processing techniques such as morphological operations and blurring Implement a motion detector for monitoring environments Implement a social distancing detector Implement a car and truck counter using highway videos This course is ideal for individuals who are People interested in implementing motion detectors or object counters or Undergraduate and postgraduate students studying Computer Graphics, Digital Image Processing or Artificial Intelligence or Data Scientists who want to increase their knowledge in Computer Vision It is particularly useful for People interested in implementing motion detectors or object counters or Undergraduate and postgraduate students studying Computer Graphics, Digital Image Processing or Artificial Intelligence or Data Scientists who want to increase their knowledge in Computer Vision.

Enroll now: Motion Detection using Python and OpenCV

Summary

Title: Motion Detection using Python and OpenCV

Price: $74.99

Average Rating: 4.7

Number of Lectures: 30

Number of Published Lectures: 30

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the basic intuition about background subtraction applied to motion detection
Implement MOG, GMG, KNN and CNT algorithms using OpenCV, as well as compare their quality and performance
Improve the quality of the results using pre-processing techniques such as morphological operations and blurring
Implement a motion detector for monitoring environments
Implement a social distancing detector
Implement a car and truck counter using highway videos

Who Should Attend

People interested in implementing motion detectors or object counters
Undergraduate and postgraduate students studying Computer Graphics, Digital Image Processing or Artificial Intelligence
Data Scientists who want to increase their knowledge in Computer Vision

Target Audiences

People interested in implementing motion detectors or object counters
Undergraduate and postgraduate students studying Computer Graphics, Digital Image Processing or Artificial Intelligence
Data Scientists who want to increase their knowledge in Computer Vision

Motion detection is a sub-area of Computer Vision that aims to identify motion in videos or in real time. This type of application can be very useful, especially for security systems, in which it is necessary to detect suspicious movements such as a thief trying to enter the house. There are several other applications, such as: traffic analysis on highways, people detection/counting, animal tracking, cyclist counting, among others. A traffic control system can use these techniques to identify the number of cars and trucks that pass through the highway daily and at certain times, so then it is possible to carry out a road maintenance plan.

In this course you will learn in practice how to use background subtraction algorithms to detect movements in videos, all step by step and using Python programming language! Check out the main topics you are going to learn, as well as the hands-on projects:

Basic theoretical intuition about the following background subtraction algorithms: Temporal Median Filter, MOG (Mixture of Gaussians), GMG (Godbehere, Matsukawa and Goldbert), KNN (K Nearest Neighbors) and CNT (Count)
Comparison of quality and performance of each algorithm
Practical project 1: motion detector to monitor environments
Practical project 2: social distancing detector to identify possible crowds of people
Practical project 3: car and truck counter on highways

At the end of the course, you will be able to create your own motion detection projects!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Course materials

Chapter 2: Background subtraction

Lecture 1: Background subtraction – intuition

Lecture 2: Temporal median filter – intuition

Lecture 3: Installing Anaconda and PyCharm

Lecture 4: Temporal median filter – implementation 1

Lecture 5: Temporal median filter – implementation 2

Lecture 6: Temporal median filter – implementation 3

Lecture 7: Other algorithms: MOG, GMC, KNN, and CNT

Lecture 8: Additional reading

Lecture 9: Image preprocessing techniques

Lecture 10: MOG, GMC, KNN and CNT – implementation 1

Lecture 11: MOG, GMC, KNN and CNT – implementation 2

Lecture 12: MOG, GMC, KNN and CNT – implementation 3

Lecture 13: MOG, GMC, KNN and CNT – implementation 4

Lecture 14: MOG, GMC, KNN and CNT – implementation 5

Lecture 15: Quality comparison 1

Lecture 16: Quality comparison 2

Lecture 17: Performance comparison

Chapter 3: Practical projects

Lecture 1: Motion detection 1

Lecture 2: Edge detection – intuition

Lecture 3: Motion detection 2

Lecture 4: Social distancing

Lecture 5: Vehicle counter 1

Lecture 6: Vehicle counter 2

Lecture 7: Vehicle counter 3

Lecture 8: Vehicle counter 4

Lecture 9: Vehicle counter 5

Chapter 4: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Jones Granatyr
Professor
Dalton Luiz Vargas
Cientista da Computação
AI Expert Academy
Instructor

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 2 votes
4 stars: 13 votes
5 stars: 35 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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