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Course Outline
Introduction to Computer Vision in Autonomous Driving
- The 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.
- Fundamental knowledge of machine learning concepts.
- Familiarity with image processing techniques.
Target Audience
- AI developers engaged in autonomous driving applications.
- Computer vision engineers specializing in real-time perception.
- Researchers and developers with an interest in automotive AI.
21 Hours
Testimonials (1)
Hands on and the practical
