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Course Outline
Foundations of Computer Vision in Autonomous Driving
- The critical function of computer vision within autonomous vehicle architectures
- Addressing challenges and finding solutions in real-time vision processing
- Core concepts: object detection, tracking mechanisms, and scene interpretation
Image Processing Essentials for Autonomous Vehicles
- Data acquisition techniques from cameras and various sensors
- Fundamental operations: image filtering, edge identification, and geometric transformations
- Developing preprocessing workflows for real-time visual tasks
Object Detection and Classification Strategies
- Extracting features using SIFT, SURF, and ORB
- Traditional detection methods: HOG and Haar cascades
- Modern deep learning approaches: CNNs, YOLO, and SSD
Lane and Road Marking Identification
- Utilizing the Hough Transform for detecting lines and curves
- Extracting Regions of Interest (ROI) for lane marking analysis
- Building lane detection systems using OpenCV and TensorFlow
Semantic Segmentation for Comprehensive Scene Understanding
- Applying semantic segmentation concepts within autonomous driving frameworks
- Advanced deep learning techniques: FCN, U-Net, and DeepLab
- Achieving real-time segmentation via deep neural networks
Obstacle and Pedestrian Recognition
- Executing real-time object detection with YOLO and Faster R-CNN
- Performing multi-object tracking using SORT and DeepSORT
- Recognizing pedestrians through HOG and deep learning models
Sensor Fusion for Improved Perception Accuracy
- Integrating vision data with LiDAR and RADAR inputs
- Employing Kalman filtering and particle filtering for data synthesis
- Enhancing perception precision through sensor fusion methodologies
Testing and Validation of Vision Systems
- Benchmarking vision models against automotive-specific datasets
- Evaluating and optimizing real-time performance metrics
- Constructing a complete vision pipeline for autonomous driving simulations
Real-World Case Studies and Applications
- Examining successful vision implementations in commercial autonomous cars
- Practical project: Building a lane and obstacle detection pipeline
- Discussing emerging trends and future directions in automotive computer vision
Recap and Future Pathways
Requirements
- Solid proficiency in Python programming
- Foundational knowledge of machine learning principles
- Basic familiarity with image processing methodologies
Target Audience
- AI developers specializing in autonomous driving solutions
- Computer vision engineers focused on real-time perception systems
- Researchers and developers with a keen interest in automotive AI
21 Hours
Testimonials (1)
Hands on and the practical
