Automotive Camera [Apply Computer vision, Deep learning] - 1

🚗 Unlock the Secrets of Automotive Camera Systems with AI & Deep Learning!

Course Title: Automotive Camera [Apply Computer Vision, Deep learning] - 1

Course Headline: Master the Art of Autonomous Driving Technology through Camera Perception!

Course Overview: Automotive cameras have revolutionized the field of Advanced Driver Assistance Systems (ADAS) and paved the way for autonomous driving. With the integration of deep learning and computer vision, these technologies have become more sophisticated than ever before. Our comprehensive course is designed to provide you with a deep understanding of how camera sensors work within the context of ADAS and autonomous vehicles, and how to apply this knowledge in real-world scenarios using Python programming.

What You'll Learn in Course 1:

Basics of ADAS and Autonomous Driving:

• Understand the core concepts and technologies that enable ADAS and autonomous vehicles.

Sensor Overview:

• Explore the various sensors used in autonomous driving, including radar, cameras, LiDAR, ultrasonic, GPS, GNSS, and IMU.

Camera Technology Deep Dive:

• Discover the intricacies of camera sensors, image sensors, sensor size, pixel density, angle of field of view (AFoV), resolution, and digital interfaces.

Camera Model and Calibration:

• Learn about the pinhole camera model and how to derive the Intrinsic and Extrinsic camera calibration matrix.

Deep Learning Models for Image Processing:

• Gain insights into state-of-the-art deep learning models like R-CNN, Fast R-CNN, Faster R-CNN, YOLOv3, SSD, Mask R-CNN, and more.

Object Tracking Techniques:

• Understand the principles of object tracking (both single and multi-object) and data association using Kalman filters.

Practical Application:

• Learn how to track multiple objects in the camera image plane.

Additional Resources:

• A curated list of books, technical papers, and web-links for further exploration.

Knowledge Check:

• Test your understanding with a quiz at the end of the course.

What You'll Learn in Course 2 (Available Separately):

Full Perception Pipeline Development:

• Step by step guidance on developing a complete camera perception pipeline using Python 3.x.

Dataset Introduction and Insights:

• Access to public datasets and understand how to use them for your projects.

Software Design with UML and Python:

• Learn to design software with object oriented programming and UML class diagrams.

Image Processing in Python:

• Implement Python classes to read and process images.

Object Detection & Classification:

• Utilize pre-trained models like YOLOv3, SSD, and Mask R-CNN for object detection and classification.

Multi-Object Tracking with Kalman Filters:

• Develop tracking of various vehicles and road users in the image plane using Kalman filters.

Visualization and Data Export:

• Learn to visualize objects and export tracking data to JSON files.

Optional Assignment:

• Challenge yourself with an assignment to solidify your skills.

Suggestion for Learners:

• If you're interested in grasping the concepts of camera perception in ADAS and autonomous driving, Course 1 is perfect for you.

• For those eager to understand both the theory behind camera perception and the practical application through programming, consider enrolling in both Course 1 and Course 2. It's highly recommended to complete Course 1 before advancing to Course 2, ensuring a strong foundation for the more complex topics.

Embark on your journey to become an expert in automotive camera systems and AI-driven vehicle perception with our specially crafted course series. 🚦🤯👨‍💻