LEARN OBSTACLE TRACKING: The Master Key to become a Self-Driving Cars Professional by Jeremy Cohen

LEARN OBSTACLE TRACKING: The Master Key to become a Self-Driving Cars Professional


Learn the rare skill that will boost your Computer Vision expertise and increment your portfolio.

What's included

✔️ The Master-Key to become a self-driving car professional
✔️ Real-world Portfolio project
✔️ One-on-one onboarding sessions (optional)
✔️ VIP contact and support

Do you need to fill a gap in Computer Vision?

Object detection alone isn't useful. Object tracking is.

Dive into AI, build a state of the art obstacle detection algorithm, learn to track obstacles through time, and learn to build a robust project.

Take your skills from okay to great!

What you'll learn

✔️ The origins of obstacle detection
✔️ The algorithms experts use today and why they're better
✔️ 2 main families of obstacle detectors and how you can use them
✔️PROJECT - Create your own solution and build on major skills
✔️
How to get the same performances as experts from your laptop without a GPU, Linux, or big storage.
✔️ 3 ways tracking will change your career
✔️ Fundamentals of obstacle association- The road to Computer Vision and Sensor Fusion

✔️ Master obstacle association - Use it in ANY Computer Vision task
✔️ Get better at reading research papers. Set your way towards expertise and be acknowledged.
✔️CHALLENGE - Code a research paper function
✔️ Deep SORT - Learn the master obstacle tracking system using Deep Learning and Artificial Intelligence
✔️ How to shift from Computer Vision to Sensor Fusion with the same tools
✔️PROJECT - Build your own bounding box tracking algorithm
✔️FINAL PROJECT - Build a real-life project that looks professional and cutting-edge.
Be ready to shine.

Object Detection

Learn the fundamentals of obstacle detection. The building block of our system.

Algorithms: HOG Detectors, Machine Learning, CNNs, YOLOv3, YOLOv4, FASTER RCNN family.

Obstacle Tracking

Association and tracking will teach you how to make sense of detections. Learn about association through time and build your own tracking project.

Algorithms
: SORT, Hungarian Algorithm, Deep SORT, occlusion, making the algorithm robust.

Real-Life Projects

We will learn cutting-edge algorithms studied in research and used in the field.

You will build your own obstacle tracking algorithm in real-life footage. It will be fast and effective.

🧐 Still need more precision? Look at the detailed summary  here.

Computer Vision at its best.

This course is the best seller of Think Autonomous.
The first students already designed their tracking algorithms and used it to get jobs in the Computer Vision or the Autonomous Tech Industry...

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Join them and master one of the most useful skills in Computer Vision.
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Abishek Sharma, Practice AI Engineering Head
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FAQs

What are the prerequisites?

The whole course is in Python 3, you need to know how to use it
It starts with obstacle detection, so you'll need to know a bit how it works. I don't reexplain everything from scratch. You'll need to at least understand how Convolutional Neural Networks work!
With Python and CNN knowledge, you're all set!

Can I do the course with no GPU?

I made the course easily accessible for everyone. It can be accessed with or without a GPU. The only difference will be the time to run your projects. Without a GPU, it can take you more time but it will be totally doable.

I'm not sure this is useful to me...

Don't you need a new project to add? Something very visual and powerful to show?
Something that recruiters will consider advanced?
You can with this one!

Can this course really help me get a job in self-driving cars?

Affirmative. In perception, obstacle association is needed everywhere!
There is Tracking, but also Sensor Fusion!
In Sensor Fusion, we can fuse data different sensors... such as LiDARs and Cameras.
Instead of associating obstacles from frame 0 to frame 1, you associate them from LiDAR space to camera space with the same algorithms!

See for yourself how this course can help you get a job in Computer Vision or Sensor Fusion:

Mastering Obstacle Tracking is being one step closer to make Computer Vision very useful. We're moving from an independent detection to a complete situation understanding; allowing for new opportunities such as behavioral prediction.

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Jeremy Cohen

Artificial Intelligence & Self-Driving Car Engineer, Head Dean of France School of AI, and Machine Learning Lecturer.
I started thinkautonomous.ai to help aspiring AI & Self-Driving Car Engineers to land their dream job. Working in the industry of the future requires skills and passion. You can build your skills here, where you'll create relevant projects that are used every day in autonomous robots & AI engines.

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