Udemy – Object Tracking using Python and OpenCV 2021-11 – Full Version

Description

Object Tracking using Python and OpenCV is a training course on object tracking using Python programming language and OpenCV library, published by Udemy Academy. This course is completely project oriented and during the training process it uses over 12 different algorithms and techniques to track objects in videos and webcams. Object tracking is a subset of computer vision that aims to track and track objects in successive frames of a video file or live stream or webcam or camera CCTV. One of the most important applications of object tracking is video protection and security systems and various office units. These systems can detect suspicious movements and alert you as soon as possible.

Many applications can be considered for tracking objects, including commercial and non-security objects, such as traffic cameras and speeding cars and motorcycles, or even monitoring movements and analyzing the tactics put in place. implemented by football players.

What you will learn in Object Tracking using Python and OpenCV

  • Tracking objects and their movements in cameras, webcams and live video files
  • Python programming language basics
  • OpenCV library and how to install, configure and use it
  • Different tracking algorithms and the unique applications and features of each
  • Differences and points of difference between object detection and object tracking
  • And …

Course specifications

Editor: Udemy
Instructors: Dalton Luiz Vargas, Jones Granatyr and IA Expert Academy
French language
Beginner level
Number of lessons: 33
Duration: 4 hours and 44 minutes

Course topics on 2022/2

Object tracking using Python and OpenCV content

Object tracking using Python and OpenCV

Programming logic
Basic Python programming

Pictures

Object tracking using Python and OpenCV

Object Tracking Using Python and OpenCV Intro Video

Installation guide

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english subtitle

Quality: 720p

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Download part 1 – 1 GB

Download part 2 – 1 GB

Download part 3 – 85 MB

File password(s): ngaur.com

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