Computer Vision for Autonomous Driving Training Course

Introduction to Computer Vision for Autonomous Driving

• The role of computer vision in autonomous vehicle systems for government applications
• Challenges and solutions in real-time vision processing for government use
• Key concepts: object detection, tracking, and scene understanding for government operations

Image Processing Fundamentals for Autonomous Vehicles for Government Use

• Image acquisition from cameras and sensors for government applications
• Basic operations: filtering, edge detection, and transformations for government use
• Preprocessing pipelines for real-time vision tasks in government systems

Object Detection and Classification for Government Applications

• Feature extraction using SIFT, SURF, and ORB for government operations
• Classical detection algorithms: HOG and Haar cascades for government use
• Deep learning approaches: CNNs, YOLO, and SSD for government applications

Lane and Road Marking Detection for Government Use

• Hough Transform for line and curve detection in government systems
• Region of interest (ROI) extraction for lane marking for government operations
• Implementing lane detection using OpenCV and TensorFlow for government use

Semantic Segmentation for Scene Understanding in Government Applications

• Understanding semantic segmentation in autonomous driving for government operations
• Deep learning techniques: FCN, U-Net, and DeepLab for government use
• Real-time segmentation using deep neural networks for government systems

Obstacle and Pedestrian Detection for Government Use

• Real-time object detection with YOLO and Faster R-CNN for government applications
• Multi-object tracking with SORT and DeepSORT for government operations
• Pedestrian recognition using HOG and deep learning models for government use

Sensor Fusion for Enhanced Perception in Government Systems

• Combining vision data with LiDAR and RADAR for government applications
• Kalman filtering and particle filtering for data integration for government use
• Improving perception accuracy with sensor fusion techniques for government operations

Evaluation and Testing of Vision Systems for Government Applications

• Benchmarking vision models with automotive datasets for government use
• Real-time performance evaluation and optimization for government systems
• Implementing a vision pipeline for autonomous driving simulation for government applications

Case Studies and Real-World Applications of Government Vision Systems

• Analyzing successful vision systems in autonomous cars for government operations
• Project: Implementing a lane and obstacle detection pipeline for government use
• Discussion: Future trends in automotive computer vision for government applications

Summary and Next Steps for Government Vision Systems