What you'll learn:
- Basics of ADAS (Advanced Driver Assistance Systems) and Autonomous Driving
- Understanding need and role of camera in ADAS and AD
- Understanding different terminologies regarding camera
- Camera Pin hole model, concept of Perspective Projection and derive homogenous equations for camera
- Concepts of Extrinsic and Intrinsic camera calibration matrix
- Understand breifly the process of doing intrinsic and extrinsic camera calibration
- Concepts of Image classfication and Image localization
- Concepts of Object detection including state of the art models - R-CNN, Fast R-CNN, Faster R-CNN, YOLOv3 and SSD
- Image segmentation, what is instance and semantic segmentation & Mask R-CNN
- Concept of multi object tracking, kalman filter, data association and how to do MOT for camera images
Perception of Environment is a very crucial and important step in the development of ADAS (Advanced Driver Assistance Systems) and also in Autonomous Driving. The main sensors that are widely accepted and used include Radar, Camera, LiDAR, and Ultrasonic.
This course focuses on Camera. Specifically, with the advancement of deep learning together with computer vision, the algorithm development approach in the field of cameras has drastically changed in the last few years.
Many new students and also people from other fields want to learn about this technology as it provides a great scope of development and job market. There are also many courses available to teach some topics of this development but in parts and pieces with only intention to teach just the individual concept.
In such a situation, even if someone understands how a specific concept works, the person finds it difficult to properly put in the form of a software module and also to be able to develop complete software from start to end which is demanded in most of the companies.
This series which contains 3 courses - is designed systematically, so that by the end of the series, you will be ready to develop any perception-based complete end-to-end software application without hesitation and with confidence.
Course 1 (This course) - focuses on theoretical foundations
Course 2A (to be published very soon) - focuses on step-by-step implementation of camera processing module and object detector modules using python 3.x and object oriented programming.
course 2B (to be published very soon) - focuses on step-by-step implementation of camera-based multi-object tracking (including Track object data structures, Kalman filters, tracker, data association, etc.) using python 3.x and object oriented programming.
Course 1 - teaches you the following content (This course)
1. Basics of ADAS and autonomous driving technology with examples
2. Understanding briefly about sensors - radar, camera, lidar, ultrasonic, GPS, GNSS, IMU for autonomous driving
3. Role of camera in detail and also various terms associated with camera – image sensor, sensor size, pixel, AFoV, resolution, digital interfaces, ego and sensor coordinate system, etc.
4. Pin hole camera model, concept & derive Intrinsic and extrinsic camera calibration matrix
5. Concept of image classification, image Localization, object detection Understanding many state of the art deep learning models like R-CNN, Fast R-CNN, Faster R-CNN, YOLOv3, SSD, Mark R-CNN, etc.
6.Concept of Object tracking (single object & multi-object tracking) in general, concept of data association, Kalman filter based tracking, Kalman filter equations
7. How to track multiple objects in the camera image plane.
8. Additional Reference – list of books, technical papers and web-links
9. Quiz
[Suggestion]:
Those who wants to learn and understand only concepts can take course 1 only.
Those who wants to learn and understand concepts and also wants to know and/or do programming of the those concepts should take all three course 1, course 2A, and course 2B.
