|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/camera-algorithm-development-course-1/
课程评论：没有评论
## 汽车摄像头（应用计算机视觉、深度学习）- 1：课程内容概述 本课程是关于自动驾驶领域**环境感知**的系列课程的第一部分，重点在于**理论基础**。通过学习本课程，您将深入了解摄像头在高级驾驶辅助系统（ADAS）和自动驾驶中的关键作用。 **课程主要内容包括：** 1. **ADAS与自动驾驶基础：** 介绍ADAS和自动驾驶技术，并提供相关实例。 2. **自动驾驶常用传感器：** 简要介绍雷达、摄像头、激光雷达、超声波、GPS、GNSS、IMU等传感器。 3. **摄像头的详细作用与术语：** 深入探讨摄像头在自动驾驶中的角色，以及图像传感器、传感器尺寸、像素、视场角（AFoV）、分辨率、数字接口、自身坐标系与传感器坐标系等相关概念。 4. **相机成像模型与标定：** 讲解针孔相机模型，并推导内在和外在相机标定矩阵。 5. **深度学习在视觉感知中的应用：** 介绍图像分类、图像定位、目标检测的概念，并深入学习R-CNN、Fast R-CNN、Faster R-CNN、YOLOv3、SSD、Mask R-CNN等一系列先进的深度学习模型。 6. **目标跟踪基础：** 掌握单目标和多目标跟踪的通用概念，以及数据关联和基于卡尔曼滤波的跟踪算法，包括卡尔曼滤波方程。 7. **多目标跟踪实现：** 学习如何在摄像头图像平面上实现多目标跟踪。 8. **拓展参考资料：** 提供书籍、技术论文和网页链接，供您进一步学习。 9. **知识检测：** 课程包含测验，以巩固所学知识。 **建议：** * 如果您只想理解概念，学习本课程（第一部分）即可。 * 如果您希望深入理解概念并学习编程实现，建议您完成本系列所有课程（第一部分 + 第二部分 A 和 B），以获得端到端软件开发的能力。
Perception of the Environment is a crucial step in the development of ADAS (Advanced Driver Assistance Systems) and Autonomous Driving. The main sensors that are widely accepted and used include Radar, Camera, LiDAR, and Ultrasonic.This course focuses on Cameras. Specifically, with the advancement of deep learning and computer vision, the algorithm development approach in the field of cameras has drastically changed in the last few years.Many new students and people from other fields want to learn about this technology as it provides a great scope of development and job market. Many courses are also available to teach some topics of this development, but they are in parts and pieces, intended to teach only 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 (available online to enrol and learn) - focuses on the 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 the 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 examples2. Understanding briefly about sensors - radar, camera, lidar, ultrasonic, GPS, GNSS, IMU for autonomous driving3. Role of the camera in detail and also various terms associated with the camera - image sensor, sensor size, pixel, AFoV, resolution, digital interfaces, ego and sensor coordinate system, etc.4. Pinhole camera model, concept & derive Intrinsic and extrinsic camera calibration matrix5. 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 equations7. How to track multiple objects in the camera image plane.8. Additional Reference - list of books, technical papers and web-links9. 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.
