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What you will learn

  • Track objects from videos and from the webcam using Python and OpenCV
  • Understand the basic intuition about tracking algorithms
  • Implement 12 tracking algorithms
  • Understand the differences between object detection and object tracking

Requirements

  • Programming logic
  • Basic Python programming

Description

Object tracking is a subarea of Computer Vision which aims to locate an object in successive frames of a video. An example of application is a video surveillance and security system, in which suspicious actions can be detected. Other examples are the monitoring of traffic on highways and also the analysis of the movement of players in a soccer match! In this last example, it is possible to trace the complete route that the player followed during the match.

To take you to this area, in this course you will learn the main object tracking algorithms using the Python language and the OpenCV library! You will learn the basic intuition about 12 (twelve) algorithms and implement them step by step! At the end of the course you will know how to apply tracking algorithms applied to videos, so you will able to develop your own projects. The following algorithms will be covered: Boosting, MIL (Multiple Instance Learning), KCF (Kernel Correlation Filters), CSRT (Discriminative Correlation Filter with Channel and Spatial Reliability), MedianFlow, TLD (Tracking Learning Detection), MOSSE (Minimum Output Sum of Squared) Error), Goturn (Generic Object Tracking Using Regression Networks), Meanshift, CAMShift (Continuously Adaptive Meanshift), Optical Flow Sparse, and Optical Flow Dense.

You’ll learn the basic intuition about all algorithms and then, we’ll implement and test them using PyCharm IDE. It’s important to emphasize that the goal of the course is to be as practical as possible, so, don’t expect too much from the theory since you are going to learn only the basic aspects of each algorithm. The purpose of showing all these algorithms is for you to have a view that different algorithms can be used according to the types of applications, so you can choose the best ones according to the problem you are trying to solve.

Who this course is for

  • Beginners who are starting to learn Computer Vision and Object Tracking
  • Undergraduate students who are studying subjects related to Artificial Intelligence
  • Anyone interested in Artificial Intelligence or Computer Vision
  • Data scientists who want to grow their portfolio

Ratings and Reviews

4.8
Avg. Rating
49 Ratings
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Review posted on Udemy
Posted 4 weeks ago
by Tolga Girici

Nice introduction to object tracking.

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Review posted on Udemy
Posted 7 months ago
by Taras Dysiuk

Thank you for a course

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Review posted on Udemy
Posted 9 months ago
by Javier Alfaro

muy descriptivas las explicaciones, programas y soportes

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Review posted on Udemy
Posted 9 months ago
by Pawel Walenda

The Instructor presents the topic in a clear, engaging, and highly interesting manner.

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Review posted on Udemy
Posted 9 months ago
by Manish Ghanshani

Very nice and detailed explanation

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Review posted on Udemy
Posted 10 months ago
by David Raleigh

Excellent topic explanation; highly helpful for anyone seeking quick access to the subject!

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Review posted on Udemy
Posted 10 months ago
by Gregory Manley

This course has been a terrific experience for me. The lessons were really helpful and straightforward. A thorough examination of the key theories would be fascinating for the course to be ideal.

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Review posted on Udemy
Posted 10 months ago
by Timothy Edwards

I am really appreciative of this course; I consider it to be the best one I have taken, and I am passionate about learning about object tracking.

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Review posted on Udemy
Posted 10 months ago
by Colette Stanberry

Outstanding guidance and explanation; among the greatest courses for in-depth learning

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Review posted on Udemy
Posted 10 months ago
by William Morrison

It was a wonderful education.  The course has a ton of resources. For a beginner, it's fairly good. Thanks!

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