|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/motion-detection-using-python-and-opencv/
课程评论：没有评论
Coursera 课程《使用 Python 和 OpenCV 进行运动检测》课程总结 本课程专注于计算机视觉中的运动检测技术，旨在帮助学员掌握在视频或实时流中识别运动的方法。运动检测在安全监控、交通分析、人口统计、动物追踪等领域具有广泛应用。 课程主要内容包括： * **运动检测理论基础**：深入讲解多种背景减除算法，包括但不限于**时间中值滤波、高斯混合模型 (MOG)、GMG (Godbehere, Matsukawa and Goldbert)、K近邻算法 (KNN) 和 CNT (Count)**。 * **算法性能对比**：对不同算法的质量和性能进行比较分析，帮助理解各自的优缺点。 * **实践项目**： * **项目一：环境监控运动检测器** * **项目二：社交距离检测器（识别人群聚集）** * **项目三：高速公路车辆（汽车和卡车）计数器** 通过本课程的学习，学员将能够**一步一步地使用 Python 编程语言**，**实操掌握背景减除算法**，并最终能够**独立开发自己的运动检测项目**。
Motion detection is a sub-area of Computer Vision that aims to identify motion in videos or in real time. This type of application can be very useful, especially for security systems, in which it is necessary to detect suspicious movements such as a thief trying to enter the house. There are several other applications, such as: traffic analysis on highways, people detection/counting, animal tracking, cyclist counting, among others. A traffic control system can use these techniques to identify the number of cars and trucks that pass through the highway daily and at certain times, so then it is possible to carry out a road maintenance plan.In this course you will learn in practice how to use background subtraction algorithms to detect movements in videos, all step by step and using Python programming language! Check out the main topics you are going to learn, as well as the hands-on projects:Basic theoretical intuition about the following background subtraction algorithms: Temporal Median Filter, MOG (Mixture of Gaussians), GMG (Godbehere, Matsukawa and Goldbert), KNN (K Nearest Neighbors) and CNT (Count)Comparison of quality and performance of each algorithmPractical project 1: motion detector to monitor environmentsPractical project 2: social distancing detector to identify possible crowds of peoplePractical project 3: car and truck counter on highwaysAt the end of the course, you will be able to create your own motion detection projects!
