Autonomous Cars: The Complete Computer Vision Course 2022

🚗 Autonomous Cars: The Complete Computer Vision Course 2022 🚀

Headline: Autonomous Cars: Computer Vision and Deep Learning

The automotive industry is on the cusp of a transformative leap from traditional human-operated vehicles to advanced self-driving, AI-powered machines. The advent of autonomous vehicles promises a future that is not only safer and more efficient but also a significant step towards reshaping human mobility. By 2035, self-driving cars are expected to save over half a million lives and contribute enormously to the global economy, with economic opportunities exceeding $1 trillion dollars. As we collectively navigate towards this driverless future, the demand for skilled engineers and researchers in autonomous vehicle technology is soaring.

Course Overview: This comprehensive course is meticulously designed to equip students with the core knowledge required to develop and understand self-driving vehicle technologies. It delves into pivotal concepts such as lane detection, traffic sign classification, vehicle/object detection, artificial intelligence, and deep learning, providing a solid foundation in autonomous vehicle control systems. While a basic understanding of programming is recommended, the course begins with fundamental programming concepts to ensure that all students, regardless of their initial expertise, can join this journey into the future of transportation.

What You Will Learn:

• OpenCV: Master the use of OpenCV, an open-source computer vision library, for real-time image and video processing.
• Deep Learning and Neural Networks: Explore the world of deep learning and neural networks to understand how they power intelligent computer vision tasks.
• Convolutional Neural Networks (CNNs): Discover the inner workings of CNNs, essential for image recognition and classification.
• YOLO (You Only Look Once): Learn about YOLO, the state-of-the-art object detection system, known for its speed and accuracy.
• HOG Feature Extraction: Understand how to use Histogram of Oriented Gradients (HOG) for object detection in images.
• Color Space Techniques: Gain expertise in working with various color spaces such as grayscale, RGB, HSV, and more, to improve image analysis.
• Edge Detection & Image Processing: Explore edge detection using Sobel, Laplacian, Canny, and other techniques to identify object boundaries.
• Affine and Projective Transformation: Master the art of correcting images through affine and projective transformations.
• Hough Transform & Masking: Learn how to apply Hough transforms for line detection and masking to focus on regions of interest.
• Background Subtraction Techniques: Experiment with background subtraction methods like KNN, MOG, MeanShift, and more.
• Kalman Filter: Understand the Kalman filter for smoother state estimates based on a series of noisy measurements.
• Segmentation Models (U-NET, SegNet): Dive into advanced segmentation models like U-NET and SegNet to accurately delineate different segments of an image.
• Semantic Segmentation: Learn about semantic segmentation techniques to classify each pixel in an image based on what it represents.
• Deep Reinforcement Learning: Implement deep reinforcement learning algorithms from scratch, not just through pre-built libraries.

Projects to Hone Your Skills:

• Road Markings Detection: Practice detecting and classifying road markings in real-time footage.
• Road Sign Detection: Develop systems capable of identifying and understanding traffic signs for safe navigation.
• Pedestrian Detection Project: Work on algorithms that can accurately detect pedestrians to enhance safety and efficiency.
• Frozen Lake Environment: Tackle a challenging problem simulation with an autonomous car navigating through a frozen lake, showcasing path planning and object detection.
• Semantic Segmentation: Apply segmentation techniques to classify different objects in an image, such as vehicle, road, sidewalk, and so on.
• Vehicle Detection: Create algorithms that can detect and identify various types of vehicles from video streams or still images.

Join us on this exciting educational voyage where you'll not only learn the concepts but also implement them from scratch. Unlike other courses that offer a mere glimpse by plugging data into libraries, this course ensures a deep understanding through hands-on experience with real-world problems. It's an opportunity to truly grasp the intricacies of autonomous vehicle technology and its applications in computer vision and deep learning.

"If you can't implement it, you don't understand it." - Richard Feynman

Enroll now and embark on a journey to master the future of transportation through advanced computer vision and deep learning techniques! 🚘👩‍💻✨