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Course Outline
Introduction to Computer Vision in Autonomous Driving
- Role of computer vision in autonomous vehicle systems
- Challenges and solutions in real-time vision processing
- Key concepts: object detection, tracking, and scene understanding
Image Processing Fundamentals for Autonomous Vehicles
- Image acquisition from cameras and sensors
- Basic operations: filtering, edge detection, and transformations
- 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
- Hough Transform for line and curve detection
- Region of interest (ROI) extraction for lane marking
- Implementing lane detection using OpenCV and TensorFlow
Semantic Segmentation for Scene Understanding
- Understanding semantic segmentation in autonomous driving
- Deep learning techniques: FCN, U-Net, and DeepLab
- Real-time segmentation using deep neural networks
Obstacle and Pedestrian Detection
- Real-time object detection with YOLO and Faster R-CNN
- Multi-object tracking with SORT and DeepSORT
- Pedestrian recognition using HOG and deep learning models
Sensor Fusion for Enhanced Perception
- Combining vision data with LiDAR and RADAR
- Kalman filtering and particle filtering for data integration
- Improving perception accuracy with sensor fusion techniques
Evaluation and Testing of Vision Systems
- Benchmarking vision models with automotive datasets
- Real-time performance evaluation and optimization
- Implementing a vision pipeline for autonomous driving simulation
Case Studies and Real-World Applications
- Analyzing successful vision systems in autonomous cars
- Project: Implementing a lane and obstacle detection pipeline
- Discussion: Future trends in automotive computer vision
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Basic understanding of machine learning concepts
- Familiarity with image processing techniques
Audience
- AI developers working on autonomous driving applications
- Computer vision engineers focusing on real-time perception
- Researchers and developers interested in automotive AI
21 Hours
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I genuinely enjoyed the hands-on approach.
