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David Silver,
Stephen Welch,
Abdullah Zaidi,
Andreas Haja,
and
Aaron Brown
Register for this course and see more details by visiting:
OpenCourser.com/course/civgqv/camer
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What's inside
Syllabus
Introduction to the instructor, and the guest Timo Rehfeld, Mercedes-Benz R&D North America, and the course overview.
Learn about the various levels of autonomy, some typical sensor sets, basics of camera technology, and an introduction into the OpenCV computer vision library.
Read more
Syllabus
Introduction to the instructor, and the guest Timo Rehfeld, Mercedes-Benz R&D North America, and the course overview.
Learn about the various levels of autonomy, some typical sensor sets, basics of camera technology, and an introduction into the OpenCV computer vision library.
Read more
Learn the collision detection basics, and estimating the TTC with Lidar and Camera.
Learn about the intensity gradient and filtering techniques; extract corners, infer features of an image, and track an object across multiple images.
Camera Based 2D Feature Tracking
Learn to improve the tracking process results by combining the Camera and Lidar output
Track an Object in 3D Space
Final Thoughts from Timo
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Skills taught are highly relevant to robotics, and areas such as self-driving cars
Also teaches the Camera Based 2D Feature Tracking
Taught by experts David Silver and Stephen Welch who are recognized for their work in the field
Another Udacity course, may be part of a larger structured program of study
Save this course
Save Camera to your list so you can find it easily later:
Save
Save
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 Camera with these
activities:
Review 'Computer Vision: Algorithms and Applications' by Richard Szeliski
Show steps
Gain a comprehensive understanding of computer vision fundamentals.
View
Computer Vision
on Amazon
Show steps
-
Read selected chapters from the book
-
Summarize key concepts and algorithms
-
Apply the concepts to practical scenarios
-
Discuss your insights with peers or the instructor
Form a Study Group with Classmates
Show steps
Enhance your learning through collaborative discussions and problem-solving with peers.
Show steps
-
Reach out to classmates and invite them to join a study group
-
Establish meeting times and a communication platform
-
Review course materials together
-
Discuss key concepts and solve problems collectively
Follow Udacity's Object Tracking Tutorial
Show steps
Reinforce your learning by working through a detailed tutorial on object tracking.
Show steps
-
Access the Udacity Object Tracking Tutorial
-
Follow the step-by-step instructions
-
Complete the tutorial to create your own object tracker
-
Optional: Explore the tutorial's discussion forum for additional support
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Object Detection with Python
Show steps
Improve your understanding of computer vision by practicing with Python.
Show steps
-
Locate and install the OpenCV library
-
Import the OpenCV library into your Python script
-
Load an image into your script
-
Use OpenCV functions to detect objects in the image
-
Display the detected objects in the image
Develop a Visual Aid for Object Tracking Concepts
Show steps
Support your understanding by creating a visual representation of key concepts.
Browse courses on
Object Tracking
Show steps
-
Identify key object tracking concepts
-
Design a visual aid that clearly illustrates these concepts
-
Use visual elements such as diagrams, graphs, or animations
-
Share your visual aid with others for feedback and discussion
Solve Camera Calibration Problems
Show steps
Develop your problem-solving abilities by tackling camera calibration challenges.
Show steps
-
Identify a camera calibration problem
-
Design a solution using mathematical and computational techniques
-
Implement the solution in a programming language
-
Test and evaluate the solution's accuracy
-
Optional: Share your solution with the community for feedback
Contribute to OpenCV's Computer Vision Projects
Show steps
Gain practical experience and contribute to the computer vision community.
Browse courses on
Computer Vision
Show steps
-
Explore OpenCV's open-source projects
-
Identify a project that aligns with your interests and skills
-
Contact the project maintainers and express your interest in contributing
-
Follow the project's contribution guidelines
Build a Lidar-Based Object Detector
Show steps
Apply your knowledge to create a real-world object detection system.
Browse courses on
Object Detection
Show steps
-
Gather the necessary hardware and software
-
Design and implement algorithms for object detection
-
Test and evaluate the performance of your system
-
Optional: Present your project at a conference or publish your findings
Review 'Computer Vision: Algorithms and Applications' by Richard Szeliski
Show steps
Gain a comprehensive understanding of computer vision fundamentals.
View
Computer Vision
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters from the book
- Summarize key concepts and algorithms
- Apply the concepts to practical scenarios
- Discuss your insights with peers or the instructor
Form a Study Group with Classmates
Show steps
Enhance your learning through collaborative discussions and problem-solving with peers.
Show steps
Show steps
Show steps
- Reach out to classmates and invite them to join a study group
- Establish meeting times and a communication platform
- Review course materials together
- Discuss key concepts and solve problems collectively
Follow Udacity's Object Tracking Tutorial
Show steps
Reinforce your learning by working through a detailed tutorial on object tracking.
Show steps
Show steps
Show steps
- Access the Udacity Object Tracking Tutorial
- Follow the step-by-step instructions
- Complete the tutorial to create your own object tracker
- Optional: Explore the tutorial's discussion forum for additional support
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Object Detection with Python
Show steps
Improve your understanding of computer vision by practicing with Python.
Show steps
Show steps
Show steps
- Locate and install the OpenCV library
- Import the OpenCV library into your Python script
- Load an image into your script
- Use OpenCV functions to detect objects in the image
- Display the detected objects in the image
Develop a Visual Aid for Object Tracking Concepts
Show steps
Support your understanding by creating a visual representation of key concepts.
Browse courses on
Object Tracking
Show steps
Show steps
Show steps
- Identify key object tracking concepts
- Design a visual aid that clearly illustrates these concepts
- Use visual elements such as diagrams, graphs, or animations
- Share your visual aid with others for feedback and discussion
Solve Camera Calibration Problems
Show steps
Develop your problem-solving abilities by tackling camera calibration challenges.
Show steps
Show steps
Show steps
- Identify a camera calibration problem
- Design a solution using mathematical and computational techniques
- Implement the solution in a programming language
- Test and evaluate the solution's accuracy
- Optional: Share your solution with the community for feedback
Contribute to OpenCV's Computer Vision Projects
Show steps
Gain practical experience and contribute to the computer vision community.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Explore OpenCV's open-source projects
- Identify a project that aligns with your interests and skills
- Contact the project maintainers and express your interest in contributing
- Follow the project's contribution guidelines
Build a Lidar-Based Object Detector
Show steps
Apply your knowledge to create a real-world object detection system.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Gather the necessary hardware and software
- Design and implement algorithms for object detection
- Test and evaluate the performance of your system
- Optional: Present your project at a conference or publish your findings
Career center
Learners who complete Camera will develop knowledge and skills
that may be useful to these careers:
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers design, develop, and test self-driving cars. This course may be useful for Autonomous Vehicle Engineers because it provides an introduction to the basics of computer vision and Lidar, which are two key technologies for self-driving cars. Additionally, the course will provide insights into how to integrate these technologies into a system.
Computer Vision Scientist
Computer Vision Scientist
Computer Vision Scientists develop algorithms and technologies that enable computers to see and interpret images and videos. This course may be useful for Computer Vision Scientists because it provides an introduction to the basics of computer vision, including image processing, feature extraction, and object tracking. Additionally, the course will discuss the basics of Lidar, which is a technology that can be used to create 3D images of the world.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer vision and robotics. This course may be useful for Research Scientists who are interested in working on these topics. The course will provide a foundation for understanding the state-of-the-art in these fields. Additionally, the course will provide insights into how to design and conduct research studies.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and deploy machine learning models. This course may be useful for Machine Learning Engineers who are interested in working on computer vision or robotics systems. The course will provide a foundation for building machine learning models that can detect objects in images and track objects as they move. Additionally, the course will discuss how to use these models to improve the performance of these systems.
Systems Engineer
Systems Engineer
Systems Engineers design, develop, and test complex systems. This course may be useful for Systems Engineers who are working on computer vision or robotics systems. The course will provide a foundation for understanding the challenges and opportunities of developing these systems. Additionally, the course will provide guidance on how to design and test these systems successfully.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers who are interested in working on computer vision or robotics systems. The course will provide a foundation for building software that can detect objects in images and track objects as they move. Additionally, the course will discuss the basics of Lidar.
Project Manager
Project Manager
Project Managers plan and execute projects. This course may be useful for Project Managers who are working on computer vision or robotics systems. The course will provide insights into the challenges and opportunities of managing these projects. Additionally, the course will provide guidance on how to plan and execute these projects successfully.
Business Analyst
Business Analyst
Business Analysts gather and analyze business requirements. This course may be useful for Business Analysts who are working on computer vision or robotics systems. The course will provide insights into the challenges and opportunities of gathering and analyzing requirements for these systems. Additionally, the course will provide guidance on how to gather and analyze requirements effectively.
Sales Engineer
Sales Engineer
Sales Engineers sell software systems to businesses. This course may be useful for Sales Engineers who are selling computer vision or robotics systems. The course will provide insights into the challenges and opportunities of selling these systems. Additionally, the course will provide guidance on how to sell these systems effectively.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test software systems for bugs and other defects. This course may be useful for Quality Assurance Analysts who are working on computer vision or robotics systems. The course will provide insights into the challenges and opportunities of testing these systems. Additionally, the course will provide guidance on how to test these systems effectively.
Robotics Software Engineer
Robotics Software Engineer
Robotics Software Engineers are tasked with designing, developing, and maintaining software systems for robots. These engineers work on everything from self-driving cars to industrial robots. This course may be useful for Robotics Software Engineers because it provides an introduction to computer vision, which is a key technology for robots. Specifically, this course will provide a foundation for building software that can detect objects in images and track objects as they move. Additionally, the course will discuss the basics of Lidar, which is another important technology for robots.
Data Scientist
Data Scientist
Data Scientists collect, analyze, and interpret data. This course may be useful for Data Scientists who are interested in working with computer vision or robotics systems. The course will provide a foundation for understanding the data that these systems generate. Additionally, the course will discuss how to use this data to improve the performance of these systems.
User Experience Designer
User Experience Designer
User Experience Designers design the user interface for websites and other products. This course may be useful for User Experience Designers who are working on computer vision or robotics systems. The course will provide insights into the challenges and opportunities of designing these systems for humans. Additionally, the course will provide guidance on how to design these systems to be easy to use and visually appealing.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be useful for Product Managers who are working on computer vision or robotics systems. The course will provide insights into the challenges and opportunities of developing these systems. Additionally, the course will provide guidance on how to launch these systems successfully.
Technical Writer
Technical Writer
Technical Writers write documentation for software systems. This course may be useful for Technical Writers who are working on computer vision or robotics systems. The course will provide insights into the challenges and opportunities of writing documentation for these systems. Additionally, the course will provide guidance on how to write clear and concise documentation that is easy to understand.
For more career information including salaries, visit:
OpenCourser.com/course/civgqv/camer
Reading list
We've selected ten 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
Camera.
Save
This comprehensive textbook provides a detailed introduction to the field of computer vision, covering both the theoretical foundations and the practical applications. It would be a valuable supplement to the course for students looking to deepen their understanding of the subject.
Save
This comprehensive textbook provides a detailed introduction to the field of computer vision, covering both the theoretical foundations and the practical applications. It would be a valuable supplement to the course for students looking to deepen their understanding of the subject.
Save
Provides a practical introduction to OpenCV, the popular open-source computer vision library. It would be particularly useful for students who want to gain hands-on experience with OpenCV and implement computer vision algorithms.
This textbook provides a comprehensive overview of computer vision, covering a wide range of topics from image formation to object recognition. It would be a valuable reference for students looking to gain a deeper understanding of the field.
This textbook provides a comprehensive overview of computer vision algorithms and applications, covering a wide range of topics from image processing to object recognition. It would be a valuable reference for students looking to gain a deeper understanding of the field.
This textbook provides a comprehensive overview of computer vision, covering a wide range of topics from image formation to object recognition. It would be a valuable reference for students looking to gain a deeper understanding of the field.
This classic textbook provides a comprehensive treatment of multiple view geometry, which fundamental topic in computer vision. It would be a valuable reference for students interested in learning about this area.
This classic textbook provides a comprehensive treatment of multiple view geometry, which fundamental topic in computer vision. It would be a valuable reference for students interested in learning about this area.
This textbook provides a comprehensive overview of digital image processing, which foundational topic for computer vision. It would be a valuable reference for students looking to gain a deeper understanding of this area.
This textbook provides a comprehensive overview of digital image processing, which foundational topic for computer vision. It would be a valuable reference for students looking to gain a deeper understanding of this area.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/civgqv/camer
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Time
30240 hours
Level
Advanced
Via
Udacity
Instructors
David Silver
Stephen Welch
Abdullah Zaidi
Andreas Haja
Aaron Brown
Language
English
Good to know
Skills taught are highly relevant to robotics, and areas such as self-driving cars
Also teaches the Camera Based 2D Feature Tracking
Taught by experts David Silver and Stephen Welch who are recognized for their work in the field
Another Udacity course, may be part of a larger structured program of study
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