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Course Outline
Introduction to Computer Vision in Autonomous Driving for Government
- The role of computer vision in autonomous vehicle systems for government applications
- Challenges and solutions in real-time vision processing for government use cases
- Key concepts: object detection, tracking, and scene understanding for enhanced public sector operations
Image Processing Fundamentals for Autonomous Vehicles for Government
- Image acquisition from cameras and sensors in government vehicles
- Basic operations: filtering, edge detection, and transformations for government-specific tasks
- Preprocessing pipelines for real-time vision tasks to support public sector workflows
Object Detection and Classification for Government
- Feature extraction using SIFT, SURF, and ORB for government applications
- Classical detection algorithms: HOG and Haar cascades tailored for government use
- Deep learning approaches: CNNs, YOLO, and SSD optimized for government operations
Lane and Road Marking Detection for Government
- Hough Transform for line and curve detection in government vehicles
- Region of interest (ROI) extraction for lane marking in public sector applications
- Implementing lane detection using OpenCV and TensorFlow for government systems
Semantic Segmentation for Scene Understanding for Government
- Understanding semantic segmentation in autonomous driving for government vehicles
- Deep learning techniques: FCN, U-Net, and DeepLab adapted for public sector use
- Real-time segmentation using deep neural networks to enhance government operations
Obstacle and Pedestrian Detection for Government
- Real-time object detection with YOLO and Faster R-CNN for government applications
- Multi-object tracking with SORT and DeepSORT to support public sector safety
- Pedestrian recognition using HOG and deep learning models for government use
Sensor Fusion for Enhanced Perception for Government
- Combining vision data with LiDAR and RADAR for government vehicles
- Kalman filtering and particle filtering for data integration in public sector applications
- Improving perception accuracy with sensor fusion techniques for government operations
Evaluation and Testing of Vision Systems for Government
- Benchmarking vision models with automotive datasets for government use
- Real-time performance evaluation and optimization for public sector systems
- Implementing a vision pipeline for autonomous driving simulation in government environments
Case Studies and Real-World Applications for Government
- Analyzing successful vision systems in autonomous cars for government fleets
- Project: Implementing a lane and obstacle detection pipeline for government vehicles
- Discussion: Future trends in automotive computer vision for public sector use
Summary and Next Steps for Government
Requirements
- Proficiency in Python programming for government
- Basic understanding of machine learning concepts
- Familiarity with image processing techniques
Audience
- AI developers working on autonomous driving applications for government
- Computer vision engineers focusing on real-time perception for government
- Researchers and developers interested in automotive AI for government
21 Hours
Testimonials (1)
I genuinely enjoyed the hands-on approach.
