Optical Systems for Autonomous Driving

Lectures:
1. Main ideas behind object detection in images, a sliding window method.
2. Object detection methods, Haar-like features (Viola-Jones detector).
3. Local binary patterns for object detection.
4. Pedestrian and vehicle detection methods, histograms of oriented gradients.
5. Road line recognition in images.
6. Convolutional neural network.
7. Keypoint detectors and descriptors (SIFT, SURF).
8. AdaBoost and support vector machines for recognising the objects in images.
9. Traffic lights recognition in images.
10. Processing the images in IR spectrum and multispectral images.
11. Depth image processing (RealSense, Kinect).
12. LIDAR and spherical image processing.
13. Summary of lecture themes.
14. Reserve.

Exercises:
1. Implementation of basic template for object detection in images.
2. Implementation of a sliding window method.
3. Preparing data for training and testing.
4. Object detection using Haar-like features.
5. Object detection using local binary patterns.
6. Object detection using histograms of oriented gradient.
7. Convolutional neural network.
8. AdaBoost and SVM for recognising the objects in images.
9. Object recognition in IR images. Image enhancements and subsequent processing.
10. Depth image processing (RealSense, Kinect).
11. LIDAR and spherical image processing.
12. Combination of detectors for autonomous cars.
13. Reserve.
14. Credit