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Duration 21 hours
Course Outline
Fundamentals of Computer Vision in Autonomous Driving
- The pivotal role of computer vision in autonomous vehicle ecosystems
- Addressing challenges and finding solutions in real-time vision processing
- Core concepts including object detection, tracking, and scene interpretation
Image Processing Essentials for Autonomous Vehicles
- Capturing images from cameras and various sensors
- Essential operations such as filtering, edge detection, and geometric transformations
- Designing preprocessing pipelines for real-time vision tasks
Object Detection and Classification Strategies
- Extracting features using SIFT, SURF, and ORB descriptors
- Applying classical detection algorithms like HOG and Haar cascades
- Exploring deep learning models such as CNNs, YOLO, and SSD
Detecting Lanes and Road Markings
- Utilizing the Hough Transform for identifying lines and curves
- Extracting Regions of Interest (ROI) for effective lane marking analysis
- Building lane detection systems using OpenCV and TensorFlow
Semantic Segmentation for Scene Comprehension
- The significance of semantic segmentation in autonomous driving
- Advanced deep learning architectures: FCN, U-Net, and DeepLab
- Achieving real-time segmentation through deep neural networks
Identifying Obstacles and Pedestrians
- Performing real-time object detection with YOLO and Faster R-CNN
- Tracking multiple objects using SORT and DeepSORT algorithms
- Recognizing pedestrians via HOG and deep learning models
Sensor Fusion for Improved Perception
- Integrating vision data with inputs from LiDAR and RADAR
- Employing Kalman and particle filters for seamless data integration
- Enhancing perception accuracy through sensor fusion methods
Evaluating and Testing Vision Systems
- Benchmarking vision models against established automotive datasets
- Assessing real-time performance and optimizing system efficiency
- Creating a vision pipeline for autonomous driving simulations
Case Studies and Practical Applications
- Examining successful vision systems deployed in autonomous cars
- Project work: Developing a lane and obstacle detection pipeline
- Exploring future trends and innovations in automotive computer vision
Conclusion and Path Forward
Requirements
- Strong proficiency in Python programming
- A solid grasp of fundamental machine learning concepts
- Experience with image processing techniques
Target Audience
- AI developers specializing in autonomous driving projects
- Computer vision engineers focused on real-time perception systems
- Researchers and developers with an interest in automotive AI
Testimonials (1)
Hands on and the practical
