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Computer Vision - Object Tracking with OpenCV and Python

Ilias Papachristos
In this 1-hour long project-based course, you will learn how to do Computer Vision Object Tracking from Videos. At the end of the project, you'll have learned how Optical and Dense Optical Flow work, how to use MeanShift and CamShist and how to do a Single...
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In this 1-hour long project-based course, you will learn how to do Computer Vision Object Tracking from Videos. At the end of the project, you'll have learned how Optical and Dense Optical Flow work, how to use MeanShift and CamShist and how to do a Single and a Multi-Object Tracking. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should have a fundamental knowledge of Python and OpenCV. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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Good to know

Know what's good
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Teaches practical skills in computer vision and object tracking using optical and dense optical flow, MeanShift, and CamShift
Taught in a hands-on manner within the browser using a pre-configured cloud desktop with Python, Jupyter, and Tensorflow pre-installed
Provides instant access to a cloud desktop with pre-installed software and data
Beginners in computer vision and object tracking will find this course accessible, as it builds a strong foundation for these topics
Requires fundamental knowledge of Python and OpenCV, which may not be suitable for absolute beginners
Access to the cloud desktop and video instructions is limited, which may interrupt the learning process

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Reviews summary

Challenging computer vision project

This hands-on project, with 1-hour long video content, teaches you about computer vision object tracking using OpenCV and Python. Previous knowledge of Python and OpenCV is recommended for success in this project. The course is currently more accessible for those in North America, but improvements are being made to provide a better experience in other regions. This project provides access to cloud desktops with pre-installed software like Python, Jupyter, and TensorFlow. You'll learn about Optical and Dense Optical Flow, MeanShift, CamShift, Single Object Tracking, and Multi-Object Tracking. The project is well-received by learners, with many highlighting the streamlined approach with pre-configured cloud desktops.
1-hour long video content
"In this 1-hour long project-based course..."
Access to cloud desktops for hands-on practice
"On Rhyme, you do projects in a hands-on manner in your browser."
"For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed."
Prior knowledge of Python and OpenCV is recommended
"In order to be successful in this project, you should have a fundamental knowledge of Python and OpenCV."
Some learners experienced technical issues
"My dudes, do not waist your time on this."
"The project has tons of error, it does not work correctly..."
"Also, it is very hard to understand what the instructor is saying, plus you are just coping the code he is writing."
Currently more accessible for learners in North America
"This course works best for learners who are based in the North America region."
"We’re currently working on providing the same experience in other regions."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Computer Vision - Object Tracking with OpenCV and Python with these activities:
Seek guidance from experienced practitioners in object tracking
Provides access to valuable insights and guidance from experienced practitioners, enhancing learning and career development.
Browse courses on Mentorship
Show steps
  • Identify potential mentors in the field of object tracking.
  • Reach out and request mentorship or guidance.
  • Attend industry events and conferences to connect with experts.
Create a visual presentation summarizing core concepts
Strengthens understanding of fundamental principles by encouraging students to summarize and present core concepts in a visual format.
Browse courses on Core Concepts
Show steps
  • Identify the key concepts covered in the course.
  • Gather relevant visuals, such as diagrams, images, or graphs.
  • Design and create a visual presentation using the collected materials.
  • Present the visual summary to reinforce learning.
Contribute to open-source object tracking projects
Enhances problem-solving and collaboration skills through hands-on experience in open-source development, fostering a sense of community in the field.
Show steps
  • Identify open-source object tracking projects.
  • Review documentation and codebase.
  • Identify areas where you can contribute.
  • Collaborate with the project maintainers.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Implement and experiment with optical flow
Provides hands-on experience implementing and experimenting with optical flow for object tracking, reinforcing concepts covered in the course.
Browse courses on Optical Flow
Show steps
  • Familiarize yourself with OpenCV's optical flow functions.
  • Implement the optical flow algorithm.
  • Experiment with different parameters to observe how they affect the results.
  • Apply optical flow to object tracking.
Participate in peer review sessions
Enhances comprehension and critical thinking skills by engaging students in peer review and discussion of object tracking approaches.
Browse courses on Peer Review
Show steps
  • Form study groups with peers.
  • Share work and provide constructive feedback on object tracking implementations.
  • Discuss different approaches and compare results.
Mentor other students in their object tracking projects
Reinforces understanding and strengthens communication skills by sharing knowledge and providing guidance to other students.
Browse courses on Mentoring
Show steps
  • Offer to mentor students who are struggling or need additional support.
  • Provide guidance on project planning, implementation, and troubleshooting.
  • Share your knowledge and experience in object tracking.
Explore advanced object tracking techniques on PyImageSearch
Expands on the course by introducing advanced object tracking techniques, broadening understanding and exposure to the field.
Browse courses on Object Tracking
Show steps
  • Access PyImageSearch tutorials on advanced object tracking.
  • Follow and understand the implementation of these techniques.
  • Experiment with the provided code and datasets.
Participate in Kaggle competitions on object tracking
Provides practical experience in applying object tracking skills to real-world problems and promotes problem-solving abilities.
Browse courses on Kaggle Competitions
Show steps
  • Identify relevant Kaggle competitions focused on object tracking.
  • Form a team or participate individually.
  • Develop and implement object tracking solutions.
  • Evaluate performance and iterate based on feedback.
Build a portfolio of object tracking projects
Provides a tangible demonstration of object tracking proficiency, showcasing skills and experience for future opportunities.
Show steps
  • Identify a range of object tracking projects of varying complexity.
  • Plan and implement these projects, showcasing different techniques.
  • Document and present the results in a portfolio format.

Career center

Learners who complete Computer Vision - Object Tracking with OpenCV and Python will develop knowledge and skills that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineers design and develop systems that enable computers to interpret and understand visual data. This course provides a solid foundation in computer vision techniques, including object tracking, which is a key component in many computer vision applications. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to build computer vision systems that can track objects in real-time.
See salaries and explore the career path for Computer Vision Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots, which often require computer vision capabilities to navigate and interact with the world around them. This course provides a foundation in computer vision techniques, including object tracking, which is essential for robots that need to track objects in real-time. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to develop robots that can perform complex tasks in a variety of environments.
See salaries and explore the career path for Robotics Engineer
Data Scientist
Data Scientists use data to solve problems and make predictions. Computer vision is a powerful tool for extracting insights from visual data, and object tracking is a key technique for analyzing video data. This course provides a foundation in computer vision techniques, including object tracking, which can be applied to a wide range of data science problems. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to build data science models that can track objects in real-time.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models, which can be used to solve a wide range of problems, including object tracking. This course provides a foundation in computer vision techniques, including object tracking, which is a key component in many machine learning applications. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to build machine learning models that can track objects in real-time.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Computer vision is a rapidly growing field, and there is a high demand for software engineers with computer vision skills. This course provides a foundation in computer vision techniques, including object tracking, which is a key component in many computer vision applications. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to build software systems that can track objects in real-time.
See salaries and explore the career path for Software Engineer
Product Manager
Product Managers are responsible for the development and launch of new products. Computer vision is a powerful tool for creating new products, and object tracking is a key technique for developing products that can interact with the real world. This course provides a foundation in computer vision techniques, including object tracking, which can be applied to a wide range of products. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to develop products that can track objects in real-time.
See salaries and explore the career path for Product Manager
User Experience Designer
User Experience Designers design and develop the user interface for products. Computer vision is a powerful tool for creating user interfaces that are intuitive and easy to use. This course provides a foundation in computer vision techniques, including object tracking, which can be used to develop user interfaces that can track objects in real-time. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to design user interfaces that are both functional and visually appealing.
See salaries and explore the career path for User Experience Designer
Technical Writer
Technical Writers create documentation for software and other technical products. Computer vision is a complex field, and there is a high demand for technical writers who can explain computer vision concepts to a non-technical audience. This course provides a foundation in computer vision techniques, including object tracking, which can be used to create documentation for a wide range of computer vision products. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to write documentation that is both accurate and easy to understand.
See salaries and explore the career path for Technical Writer
Quality Assurance Analyst
Quality Assurance Analysts test software and other products to ensure that they meet quality standards. Computer vision is a powerful tool for automating quality assurance tasks, and object tracking is a key technique for testing products that interact with the real world. This course provides a foundation in computer vision techniques, including object tracking, which can be applied to a wide range of quality assurance tasks. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to automate quality assurance tasks and improve the quality of software products.
See salaries and explore the career path for Quality Assurance Analyst
Technical Support Engineer
Technical Support Engineers provide technical support to customers who are using software and other products. Computer vision is a complex field, and there is a high demand for technical support engineers who can help customers troubleshoot computer vision problems. This course provides a foundation in computer vision techniques, including object tracking, which can be used to troubleshoot a wide range of computer vision products. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to provide technical support to customers who are using computer vision products.
See salaries and explore the career path for Technical Support Engineer
Sales Engineer
Sales Engineers sell software and other products to customers. Computer vision is a rapidly growing field, and there is a high demand for sales engineers who can explain computer vision concepts to potential customers. This course provides a foundation in computer vision techniques, including object tracking, which can be used to sell a wide range of computer vision products. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to sell computer vision products to potential customers.
See salaries and explore the career path for Sales Engineer
Marketer
Marketers develop and execute marketing campaigns to promote products and services. Computer vision is a powerful tool for creating marketing campaigns that are targeted and effective. This course provides a foundation in computer vision techniques, including object tracking, which can be used to create marketing campaigns that track customers' behavior and interests. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to develop marketing campaigns that are both effective and engaging.
See salaries and explore the career path for Marketer
Business Analyst
Business Analysts analyze business processes and make recommendations for improvements. Computer vision is a powerful tool for automating business processes and improving efficiency. This course provides a foundation in computer vision techniques, including object tracking, which can be used to automate a wide range of business processes. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to analyze business processes and make recommendations for improvements that can be automated using computer vision.
See salaries and explore the career path for Business Analyst
Project Manager
Project Managers plan, execute, and close projects. Computer vision is a complex field, and there is a high demand for project managers who can manage computer vision projects. This course provides a foundation in computer vision techniques, including object tracking, which can be used to manage a wide range of computer vision projects. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to plan, execute, and close computer vision projects successfully.
See salaries and explore the career path for Project Manager
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make decisions. Computer vision is a powerful tool for extracting insights from visual data, and object tracking is a key technique for analyzing video data. This course provides a foundation in computer vision techniques, including object tracking, which can be applied to a wide range of data analysis problems. By learning about the different object tracking algorithms and how to implement them using OpenCV and Python, you will gain the skills necessary to collect, analyze, and interpret visual data.
See salaries and explore the career path for Data Analyst

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Computer Vision - Object Tracking with OpenCV and Python.
Cover image
Computer Vision
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers a wide range of topics, from image formation and processing to object detection and recognition. It would be a valuable resource for anyone who wants to learn more about computer vision.
Computer Vision
Paperback
Check
Computer Vision
Kindle Edition
Check
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, from convolutional neural networks to recurrent neural networks. It would be a valuable resource for anyone who wants to learn more about deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Probability and Statistics for Computer Science
Save
Provides a comprehensive overview of computer vision. It covers a wide range of topics, from image formation to object recognition. It would be a valuable resource for anyone who wants to learn more about computer vision.
Probability and Statistics for Computer Science
Paperback
$$$
Probability and Statistics for Computer Science
Kindle Edition
$$$
Cover image
Concurrent NumPy in Python
Save
Provides a practical introduction to deep learning for computer vision. It covers a wide range of topics, from convolutional neural networks to generative adversarial networks. It would be a valuable resource for anyone who wants to learn more about deep learning for computer vision.
Concurrent NumPy in Python: Faster NumPy With BLAS,...
Paperback
$$
Concurrent NumPy in Python: Faster NumPy With BLAS,...
Kindle Edition
$$
Cover image
Learning OpenCV 3
Save
Provides a practical introduction to OpenCV, a popular open-source computer vision library. It covers a wide range of topics, from image processing to object detection and recognition. It would be a valuable resource for anyone who wants to learn more about OpenCV.
Learning OpenCV 3: Computer Vision in C++ with the...
Paperback
$$$
Learning OpenCV 3: Computer Vision in C++ with the...
Kindle Edition
$$$
Cover image
Scientific Computing with Python - Second Edition
Save
Provides a practical introduction to computer vision with Python. It covers a wide range of topics, from image processing to object detection and recognition. It would be a valuable resource for anyone who wants to learn more about computer vision with Python.
Scientific Computing with Python - Second Edition:...
Paperback
$$

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Time
2 hours
Via
Coursera
Institutions
Rhyme
Coursera Project Network
Instructor
Ilias Papachristos
Good to know
Teaches practical skills in computer vision and object tracking using optical and dense optical flow, MeanShift, and CamShift
Taught in a hands-on manner within the browser using a pre-configured cloud desktop with Python, Jupyter, and Tensorflow pre-installed
Provides instant access to a cloud desktop with pre-installed software and data
Beginners in computer vision and object tracking will find this course accessible, as it builds a strong foundation for these topics
Requires fundamental knowledge of Python and OpenCV, which may not be suitable for absolute beginners
Access to the cloud desktop and video instructions is limited, which may interrupt the learning process
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