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Course Outline
Overview of Computer Vision Applications in Autonomous Transportation Systems
- Functionality of computer vision within automated vehicle architectures
- Operational challenges and mitigation strategies for real-time image processing
- Core methodologies: object identification, trajectory tracking, and environmental comprehension
Foundational Image Processing Techniques for Automated Vehicles
- Data ingestion from optical cameras and auxiliary sensors
- Fundamental operations: noise filtering, edge characterization, and geometric transformations
- Preprocessing workflows designed for efficient real-time vision tasks
Object Detection and Classification Methodologies
- Feature extraction techniques including SIFT, SURF, and ORB
- Conventional detection algorithms: Histogram of Oriented Gradients (HOG) and Haar cascades
- Modern approaches utilizing Convolutional Neural Networks (CNNs), YOLO, and Single-Shot Detector (SSD) models
Detection of Lane Boundaries and Road Markings
- Application of the Hough Transform for linear and curvilinear structure identification
- Extraction of Regions of Interest (ROI) specific to lane markings
- Implementation of lane detection protocols using OpenCV and TensorFlow frameworks, for government-funded research and development
Semantic Segmentation for Comprehensive Scene Analysis
- Principles of semantic segmentation in automated driving contexts
- Deep learning architectures: Fully Convolutional Networks (FCN), U-Net, and DeepLab
- Execution of real-time segmentation via deep neural networks
Identification of Obstacles and Pedestrians
- Real-time object detection utilizing YOLO and Faster R-CNN architectures
- Multi-object tracking methodologies employing SORT and DeepSORT algorithms
- Pedestrian recognition systems leveraging HOG features and deep learning models
Sensor Fusion for Improved Perceptual Accuracy
- Integration of visual data streams with LiDAR and RADAR inputs
- Data correlation techniques using Kalman and particle filtering methods
- Enhancement of perception reliability through advanced sensor fusion strategies
Assessment and Validation of Vision Systems
- Benchmarking vision algorithms against established automotive datasets
- Evaluation and optimization of real-time performance metrics
- Deployment of vision pipelines within autonomous driving simulation environments for government compliance and testing purposes
Case Studies and Operational Applications
- Analysis of proven computer vision systems in commercial autonomous vehicles
- Practical exercise: Construction of a lane and obstacle detection workflow
- Strategic discussion: Emerging trends in automotive computer vision technology
Executive Summary and Strategic Next Steps
Requirements
- Demonstrated competency in Python development
- Foundational knowledge of machine learning principles
- Experience with digital image processing methodologies
Target Audience
- Artificial intelligence specialists developing autonomous vehicle systems for government applications
- Computer vision engineers dedicated to real-time situational awareness
- Researchers and technical staff engaged in automotive artificial intelligence initiatives
21 Hours
Testimonials (1)
Hands on and the practical
