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Duration 21 hours
Course Outline
Introduction to Computer Vision in Autonomous Driving
- The critical role of computer vision in autonomous vehicle systems
- Addressing challenges and solutions in real-time vision processing
- Core concepts: object detection, tracking, and scene understanding
Image Processing Fundamentals for Autonomous Vehicles
- Acquiring images from cameras and various sensors
- Fundamental operations: filtering, edge detection, and image transformations
- Designing preprocessing pipelines for real-time vision tasks
Object Detection and Classification
- Feature extraction using SIFT, SURF, and ORB
- Classical detection algorithms: HOG and Haar cascades
- Deep learning approaches: CNNs, YOLO, and SSD
Lane and Road Marking Detection
- Using the Hough Transform for line and curve detection
- Extracting regions of interest (ROI) for lane marking analysis
- Implementing lane detection using OpenCV and TensorFlow
Semantic Segmentation for Scene Understanding
- Understanding semantic segmentation within the context of autonomous driving
- Advanced deep learning techniques: FCN, U-Net, and DeepLab
- Achieving real-time segmentation with deep neural networks
Obstacle and Pedestrian Detection
- Real-time object detection using YOLO and Faster R-CNN
- Multi-object tracking with SORT and DeepSORT
- Pedestrian recognition leveraging HOG and deep learning models
Sensor Fusion for Enhanced Perception
- Integrating vision data with LiDAR and RADAR inputs
- Applying Kalman filtering and particle filtering for data integration
- Enhancing perception accuracy through sensor fusion techniques
Evaluation and Testing of Vision Systems
- Benchmarking vision models against automotive datasets
- Conducting real-time performance evaluation and optimization
- Building a vision pipeline for autonomous driving simulations
Case Studies and Real-World Applications
- Analyzing successful vision systems deployed in autonomous cars
- Practical Project: Building a lane and obstacle detection pipeline
- Discussion: Exploring future trends in automotive computer vision
Summary and Next Steps
Requirements
- Solid proficiency in Python programming
- Foundational knowledge of machine learning principles
- Familiarity with standard image processing methods
Target Audience
- AI developers engaged in autonomous driving projects
- Computer vision specialists focused on real-time perception systems
- Researchers and developers with an interest in automotive AI
Testimonials (1)
Hands on and the practical
