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Daniel Romaniuk
In this one hour long project-based course, you will tackle a real-world computer vision problem. We will be locating and tracking a target in a video shot with a digital camera. We will encounter some of the classic challenges that make computer vision difficult: noisy sensor data, objects that change shape, and occlusion (object hidden from view).
We will tackle these challenges with an artificial intelligence technique called a particle filter.
By the end of this project, you will have coded a particle filter from scratch using Python and numpy.
Read more
In this one hour long project-based course, you will tackle a real-world computer vision problem. We will be locating and tracking a target in a video shot with a digital camera. We will encounter some of the classic challenges that make computer vision difficult: noisy sensor data, objects that change shape, and occlusion (object hidden from view).
We will tackle these challenges with an artificial intelligence technique called a particle filter.
By the end of this project, you will have coded a particle filter from scratch using Python and numpy.
Read more
In this one hour long project-based course, you will tackle a real-world computer vision problem. We will be locating and tracking a target in a video shot with a digital camera. We will encounter some of the classic challenges that make computer vision difficult: noisy sensor data, objects that change shape, and occlusion (object hidden from view).
We will tackle these challenges with an artificial intelligence technique called a particle filter.
By the end of this project, you will have coded a particle filter from scratch using Python and numpy.
Note: 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.
Register for this course and see more details by visiting:
OpenCourser.com/course/fij4uc/tracking
What's inside
Syllabus
Project Overview
In this one hour long project-based course, you will tackle a real-world computer vision problem. We will be locating and tracking a target in a video shot with a digital camera. We will encounter some of the classic challenges that make computer vision difficult: noisy sensor data, objects that change shape, and occlusion (object hidden from view).
We will tackle these challenges with an artificial intelligence technique called a particle filter.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Daniele Romaniuk, who are recognized for their work in computer vision
Designed for a one-hour, project-based format
Examines a real-world computer vision problem
Explores classic computer vision challenges, such as noisy sensor data, shape-changing objects, and occlusion
Develops a particle filter from scratch using Python and numpy
Note: This course is designed for learners in the North America region
Save this course
Save Tracking Objects in Video with Particle Filters to your list so you can find it easily later:
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Reviews summary
Difficult course with code issues
According to students, this difficult course has code issues that may hinder progress. The material may be too advanced for some learners and lacks fundamental knowledge. You can still follow the project, but don't expect to get a deep understanding of particle filter tracking.
Project is simple
"IThe project is relatively easy but at the expense of being somewhat shallow w/r to the underlying fundamental knowledge"
Course is challenging
"The author don' have idea what he is teaching"
Code is unreliable
"Code barely worked"
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 Tracking Objects in Video with Particle Filters with these
activities:
Review basic probability and statistics concepts
Show steps
Ensure a solid understanding of probability and statistics concepts, which form the foundation of particle filters.
Browse courses on
Probability
Show steps
-
Review notes or textbooks on probability and statistics
-
Work through practice problems or exercises
Probabilistic Robotics
Show steps
Review the basics of probabilistic programming and its applications in computer vision.
View
Probabilistic Robotics (Intelligent Robotics...
on Amazon
Show steps
Follow tutorials on particle filtering
Show steps
Explore and follow tutorials on particle filtering to supplement your knowledge.
Browse courses on
Particle Filter
Show steps
-
Search for online tutorials on particle filtering
-
Follow and complete one or more tutorials
-
Take notes or summarize the key concepts
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a visual representation of how a particle filter works
Show steps
Develop a visual representation or diagram of how particle filters work, which can help solidify your understanding of the concept.
Browse courses on
Particle Filter
Show steps
-
Gather information and resources about particle filters
-
Design a visual representation or diagram
-
Create the visual representation
Work through practice problems on particle filters
Show steps
Sharpen your understanding of particle filters by working through practice problems and exercises.
Browse courses on
Particle Filter
Show steps
-
Find practice problems or exercises on particle filters
-
Attempt to solve the problems or exercises on your own
-
Review and correct your solutions
Implement particle filters from scratch
Show steps
Practice implementing particle filters by coding it from scratch in Python.
Browse courses on
Particle Filter
Show steps
Review basic probability and statistics concepts
Show steps
Ensure a solid understanding of probability and statistics concepts, which form the foundation of particle filters.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review notes or textbooks on probability and statistics
- Work through practice problems or exercises
Probabilistic Robotics
Show steps
Review the basics of probabilistic programming and its applications in computer vision.
View
Probabilistic Robotics (Intelligent Robotics...
on Amazon
Show steps
Show steps
Show steps
Follow tutorials on particle filtering
Show steps
Explore and follow tutorials on particle filtering to supplement your knowledge.
Browse courses on
Particle Filter
Show steps
Show steps
Show steps
- Search for online tutorials on particle filtering
- Follow and complete one or more tutorials
- Take notes or summarize the key concepts
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a visual representation of how a particle filter works
Show steps
Develop a visual representation or diagram of how particle filters work, which can help solidify your understanding of the concept.
Browse courses on
Particle Filter
Show steps
Show steps
Show steps
- Gather information and resources about particle filters
- Design a visual representation or diagram
- Create the visual representation
Work through practice problems on particle filters
Show steps
Sharpen your understanding of particle filters by working through practice problems and exercises.
Browse courses on
Particle Filter
Show steps
Show steps
Show steps
- Find practice problems or exercises on particle filters
- Attempt to solve the problems or exercises on your own
- Review and correct your solutions
Implement particle filters from scratch
Show steps
Practice implementing particle filters by coding it from scratch in Python.
Browse courses on
Particle Filter
Show steps
Show steps
Show steps
Career center
Learners who complete Tracking Objects in Video with Particle Filters will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer specializes in developing computer vision software that enables machines to see and interpret images and videos. They use techniques such as particle filters to solve problems such as object detection, tracking, and recognition. This course provides a foundational understanding of particle filters, a key technology used by Computer Vision Engineers.
Data Scientist
Data Scientist
Data Scientists use a combination of machine learning techniques, including particle filters, to extract insights from data. They may specialize in fields such as computer vision, natural language processing, or predictive analytics. This course provides a foundation in particle filters, a technique that can help Data Scientists solve complex problems in a variety of industries.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, implement, and maintain machine learning models for a variety of purposes, including object detection and tracking. They use techniques such as particle filters to improve the performance and accuracy of these models. This course helps build a foundation in particle filters, a fundamental technique in the field of Machine Learning Engineering.
Software Engineer
Software Engineer
Software Engineers may specialize in developing software for computer vision applications, such as object detection and tracking. They use techniques such as particle filters to solve complex problems related to image and video processing. This course provides a foundation in particle filters, a valuable skill for Software Engineers working in the field of computer vision.
Robotics Engineer
Robotics Engineer
Robotics Engineers design and build robots that can perform tasks such as object manipulation and navigation. They use techniques such as particle filters to improve the accuracy and performance of these robots. This course helps build a foundation in particle filters, a useful technique for Robotics Engineers to master.
Research Scientist
Research Scientist
Research Scientists may specialize in computer vision, machine learning, or robotics. They often use particle filters in their research to solve problems related to object detection, tracking, and recognition. This course provides a foundation in particle filters, a valuable technique for Research Scientists in these fields.
Data Analyst
Data Analyst
Data Analysts use statistical techniques to extract insights from data. They may specialize in fields such as computer vision or machine learning. This course may be useful for Data Analysts who want to learn about particle filters, a technique that can help them solve complex data analysis problems.
Product Manager
Product Manager
Product Managers may work on products related to computer vision or machine learning. They need to understand the technical aspects of these technologies in order to make informed decisions about product development. This course may be helpful for Product Managers who want to learn about particle filters, a technique used in computer vision and machine learning.
Business Analyst
Business Analyst
Business Analysts may work on projects related to computer vision or machine learning. They need to understand the business implications of these technologies in order to make recommendations to stakeholders. This course may be helpful for Business Analysts who want to learn about particle filters, a technique used in computer vision and machine learning.
Technical Writer
Technical Writer
Technical Writers may specialize in writing about computer vision or machine learning. They need to understand the technical aspects of these technologies in order to write clear and accurate documentation. This course may be helpful for Technical Writers who want to learn about particle filters, a technique used in computer vision and machine learning.
Project Manager
Project Manager
Project Managers may work on projects related to computer vision or machine learning. They need to understand the technical aspects of these technologies in order to manage projects effectively. This course may be helpful for Project Managers who want to learn about particle filters, a technique used in computer vision and machine learning.
Sales Engineer
Sales Engineer
Sales Engineers may sell products related to computer vision or machine learning. They need to understand the technical aspects of these technologies in order to effectively demonstrate and explain them to customers. This course may be helpful for Sales Engineers who want to learn about particle filters, a technique used in computer vision and machine learning.
Marketing Manager
Marketing Manager
Marketing Managers may work on marketing campaigns related to computer vision or machine learning. They need to understand the technical aspects of these technologies in order to develop effective marketing materials. This course may be helpful for Marketing Managers who want to learn about particle filters, a technique used in computer vision and machine learning.
Customer Success Manager
Customer Success Manager
Customer Success Managers may work with customers who use products related to computer vision or machine learning. They need to understand the technical aspects of these technologies in order to provide support and training to customers. This course may be helpful for Customer Success Managers who want to learn about particle filters, a technique used in computer vision and machine learning.
Consultant
Consultant
Consultants may work on projects related to computer vision or machine learning. They need to understand the technical aspects of these technologies in order to provide advice to clients. This course may be helpful for Consultants who want to learn about particle filters, a technique used in computer vision and machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/fij4uc/tracking
Reading list
We've selected eight 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
Tracking Objects in Video with Particle Filters.
Provides a comprehensive overview of computer vision, covering a wide range of topics from image formation to object recognition. It would be a valuable resource for anyone looking to learn more about the fundamentals of computer vision.
Classic textbook on computer vision, covering a wide range of topics from image formation to object recognition. It would be a valuable resource for anyone looking to learn more about the fundamentals of computer vision.
Save
Comprehensive introduction to computer vision, covering a wide range of topics from image formation to object recognition. It would be a valuable resource for anyone looking to learn more about the fundamentals of computer vision.
Save
Provides a comprehensive overview of deep learning, covering a wide range of topics from neural networks to convolutional neural networks. It would be a valuable resource for anyone looking to learn more about the fundamentals of deep learning.
Provides a comprehensive overview of information theory, covering a wide range of topics from entropy to channel capacity. It would be a valuable resource for anyone looking to learn more about the fundamentals of information theory.
Provides a comprehensive overview of pattern recognition and machine learning, covering a wide range of topics from supervised learning to unsupervised learning. It would be a valuable resource for anyone looking to learn more about the fundamentals of pattern recognition and machine learning.
Provides a thorough introduction to probabilistic robotics, including particle filters. It would be a helpful resource for anyone looking to learn more about the theory and practice of particle filters.
Provides a comprehensive overview of Bayesian filtering and smoothing, covering a wide range of topics from theory to practice. It would be a valuable resource for anyone looking to learn more about the use of Bayesian filtering and smoothing in computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fij4uc/tracking
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Effort
1 hour
Level
Advanced
Via
Coursera
Institution
Coursera Project Network
Instructor
Daniel Romaniuk
Language
English
Good to know
Taught by Daniele Romaniuk, who are recognized for their work in computer vision
Designed for a one-hour, project-based format
Examines a real-world computer vision problem
Explores classic computer vision challenges, such as noisy sensor data, shape-changing objects, and occlusion
Develops a particle filter from scratch using Python and numpy
Note: This course is designed for learners in the North America region
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